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10 Current Database Research Topic Ideas in 2024

Home Blog Database 10 Current Database Research Topic Ideas in 2024

As we head towards the second half of 2024, the world of technology evolves at a rapid pace. With the rise of AI and blockchain, the demand for data, its management and the need for security increases rapidly. A logical consequence of these changes is the way fields like database security research topics and DBMS research have come up as the need of the hour.

With new technologies and techniques emerging day-by-day, staying up-to-date with the latest trends in database research topics is crucial. Whether you are a student, researcher, or industry professional, we recommend taking our Database Certification courses to stay current with the latest research topics in DBMS.

In this blog post, we will introduce you to 10 current database research topic ideas that are likely to be at the forefront of the field in 2024. From blockchain-based database systems to real-time data processing with in-memory databases, these topics offer a glimpse into the exciting future of database research.

So, get ready to dive into the exciting world of databases and discover the latest developments in database research topics of 2024!

Blurring the Lines between Blockchains and Database Systems 

The intersection of blockchain technology and database systems offers fertile new grounds to anyone interested in database research.

As blockchain gains popularity, many thesis topics in DBMS[1] are exploring ways to integrate both fields. This research will yield innovative solutions for data management. Here are 3 ways in which these two technologies are being combined to create powerful new solutions:

Immutable Databases: By leveraging blockchain technology, it’s possible to create databases to be immutable. Once data has been added to such a database, it cannot be modified or deleted. This is particularly useful in situations where data integrity is critical, such as in financial transactions or supply chain management.

Decentralized Databases: Blockchain technology enables the creation of decentralized databases. Here data is stored on a distributed network of computers rather than in a central location. This can help to improve data security and reduce the risk of data loss or corruption.

Smart Contracts: Smart contracts are self-executing contracts with the terms of the agreement between buyer and seller being directly written into lines of code. By leveraging blockchain technology, it is possible to create smart contracts that are stored and executed on a decentralized database, making it possible to automate a wide range of business processes.

Childhood Obesity: Data Management 

Childhood obesity is a growing public health concern, with rates of obesity among children and adolescents rising around the world. To address this issue, it’s crucial to have access to comprehensive data on childhood obesity. Analyzing information on prevalence, risk factors, and interventions is a popular research topic in DBMS these days.

Effective data management is essential for ensuring that this information is collected, stored, and analyzed in a way that is useful and actionable. This is one of the hottest DBMS research paper topics. In this section, we will explore the topic of childhood obesity data management.

A key challenge to childhood obesity data management is ensuring data consistency. This is difficult as various organizations have varied methods for measuring and defining obesity. For example:

Some may use body mass index (BMI) as a measure of obesity.

Others may use waist circumference or skinfold thickness.   Another challenge is ensuring data security and preventing unauthorized access. To protect the privacy and confidentiality of individuals, it is important to ensure appropriate safeguards are in place. This calls for database security research and appropriate application.

Application of Computer Database Technology in Marketing

Leveraging data and analytics allows businesses to gain a competitive advantage in this digitized world today. With the rising demand for data, the use of computer databases in marketing has gained prominence.

The application of database capabilities in marketing has really come into its own as one of the most popular and latest research topics in DBMS[2]. In this section, we will explore how computer database technology is being applied in marketing, and the benefits this research can offer.

Customer Segmentation: Storage and analysis of customer data makes it possible to gain valuable insights. It allows businesses to identify trends in customer behavior, preferences and demographics. This information can be utilized to create highly targeted customer segments. This is how businesses can tailor their marketing efforts to specific groups of customers.

Personalization: Computer databases can be used to store and analyze customer data in real-time. In this way, businesses can personalize their marketing and offers based on individual customer preferences. This can help increase engagement and loyalty among customers, thereby driving greater revenue for businesses.

Predictive Analytics: Advanced analytics techniques such as machine learning and predictive modeling can throw light on patterns in customer behavior. This can even be used to predict their future actions. This information can be used to create more targeted marketing campaigns, and to identify opportunities for cross-selling and upselling.

Database Technology in Sports Competition Information Management

Database technology has revolutionized the way in which sports competition information is managed and analyzed. With the increasing popularity of sports around the world, there is a growing need for effective data management systems that can collect, store, and analyze large volumes of relevant data. Thus, researching database technologies[3] is vital to streamlining operations, improving decision-making, and enhancing the overall quality of events.

Sports organizations can use database technology to collect and manage a wide range of competition-related data such as: 

Athlete and team information,

competition schedules and results,

performance metrics, and

spectator feedback.

Collating this data in a distributed database lets sports organizations easily analyze and derive valuable insights. This is emerging as a key DBMS research paper topic.

Database Technology for the Analysis of Spatio-temporal Data

Spatio-temporal data refers to data which has a geographic as well as a temporal component. Meteorological readings, GPS data, and social media content are prime examples of this diverse field. This data can provide valuable insights into patterns and trends across space and time. However, its multidimensional nature makes analysis be super challenging. It’s no surprise that this has become a hot topic for distributed database research[4].

In this section, we will explore how database technology is being used to analyze spatio-temporal data, and the benefits this research offers.

Data Storage and Retrieval: Spatio-temporal data tends to be very high-volume. Advances in database technology are needed to make storage, retrieval and consumption of such information more efficient. A solution to this problem will make such data more available. It will then be easily retrievable and usable by a variety of data analytics tools.

Spatial Indexing: Database technology can create spatial indexes to enable faster queries on spatio-temporal data. This allows analysts to quickly retrieve data for specific geographic locations or areas of interest, and to analyze trends across these areas.

Temporal Querying: Distributed database research can also enable analysts to analyze data over specific time periods. This facilitates the identification of patterns over time. Ultimately, this enhances our understanding of how these patterns evolve over various seasons.

Artificial Intelligence and Database Technology

Artificial intelligence (AI) is another sphere of technology that’s just waiting to be explored. It hints at a wealth of breakthroughs which can change the entire world. It’s unsurprising that the combination of AI with database technology is such a hot topic for database research papers[5] in modern times. 

By using AI to analyze data, organizations can identify patterns and relationships that might not be apparent through traditional data analysis methods. In this section, we will explore some of the ways in which AI and database technology are being used together. We’ll also discuss the benefits that this amalgamation can offer.

Predictive Analytics: By analyzing large volumes of organizational and business data, AI can generate predictive models to forecast outcomes. For example, AI can go through customer data stored in a database and predict who is most likely to make a purchase in the near future.

Natural Language Processing: All businesses have huge, untapped wells of valuable information in the form of customer feedback and social media posts. These types of data sources are unstructured, meaning they don’t follow rigid parameters. By using natural language processing (NLP) techniques, AI can extract insights from this data. This helps organizations understand customer sentiment, preferences and needs.

Anomaly Detection: AI can be used to analyze large volumes of data to identify anomalies and outliers. Then, a second round of analysis can be done to pinpoint potential problems or opportunities. For example, AI can analyze sensor data from manufacturing equipment and detect when equipment is operating outside of normal parameters.

Data Collection and Management Techniques of a Qualitative Research Plan

Any qualitative research calls for the collection and management of empirical data. A crucial part of the research process, this step benefits from good database management techniques. Let’s explore some thesis topics in database management systems[6] to ensure the success of a qualitative research plan.

Interviews: This is one of the most common methods of data collection in qualitative research. Interviews can be conducted in person, over the phone, or through video conferencing. A standardized interview guide ensures the data collected is reliable and accurate. Relational databases, with their inherent structure, aid in this process. They are a way to enforce structure onto the interviews’ answers.

Focus Groups: Focus groups involve gathering a small group of people to discuss a particular topic. These generate rich data by allowing participants to share their views in a group setting. It is important to select participants who have knowledge or experience related to the research topic.

Observations: Observations involve observing and recording events in a given setting. These can be conducted openly or covertly, depending on the research objective and setting. To ensure that the data collected is accurate, it is important to develop a detailed observation protocol that outlines what behaviors or events to observe, how to record data, and how to handle ethical issues.

Database Technology in Video Surveillance System 

Video surveillance systems are used to monitor and secure public spaces, workplaces, even homes. With the increasing demand for such systems, it’s important to have an efficient and reliable way to store, manage and analyze the data generated. This is where database topics for research paper [7] come in.

By using database technology in video surveillance systems, it is possible to store and manage large amounts of video data efficiently. Database management systems (DBMS) can be used to organize video data in a way that is easily searchable and retrievable. This is particularly important in cases where video footage is needed as evidence in criminal investigations or court cases.

In addition to storage and management, database technology can also be used to analyze video data. For example, machine learning algorithms can be applied to video data to identify patterns and anomalies that may indicate suspicious activity. This can help law enforcement agencies and security personnel to identify and respond to potential threats more quickly and effectively.

Application of Java Technology in Dynamic Web Database Technology 

Java technology has proven its flexibility, scalability, and ease of use over the decades. This makes it widely used in the development of dynamic web database applications. In this section, we will explore research topics in DBMS[8] which seek to apply Java technology in databases.

Java Server Pages (JSP): JSP is a Java technology that is used to create dynamic web pages that can interact with databases. It allows developers to embed Java code within HTML scripts, thereby enabling dynamic web pages. These can interact with databases in real-time, and aid in data collection and maintenance.

Java Servlets: Java Servlets are Java classes used to extend the functionality of web servers. They provide a way to handle incoming requests from web browsers and generate dynamic content that can interact with databases.

Java Database Connectivity (JDBC): JDBC is a Java API that provides a standard interface for accessing databases. It allows Java applications to connect to databases. It can SQL queries to enhance, modify or control the backend database. This enables developers to create dynamic web applications.

Online Multi Module Educational Administration System Based on Time Difference Database Technology 

With the widespread adoption of remote learning post-COVID, online educational systems are gaining popularity at a rapid pace. A ubiquitous challenge these systems face is managing multiple modules across different time zones. This is one of the latest research topics in database management systems[9].

Time difference database technology is designed to handle time zone differences in online systems. By leveraging this, it’s possible to create a multi-module educational administration system that can handle users from different parts of the world, with different time zones.

This type of system can be especially useful for online universities or other educational institutions that have a global reach:

It makes it possible to schedule classes, assignments and other activities based on the user's time zone, ensuring that everyone can participate in real-time.

In addition to managing time zones, a time difference database system can also help manage student data, course materials, grades, and other important information.

Why is it Important to Study Databases?

Databases are the backbone of many modern technologies and applications, making it essential for professionals in various fields to understand how they work. Whether you're a software developer, data analyst or a business owner, understanding databases is critical to success in today's world. Here are a few reasons why it is important to study databases and more database topics for research paper should be published:

Efficient Data Management

Databases enable the efficient storage, organization, and retrieval of data. By studying databases, you can learn how to design and implement effective data management systems that can help organizations store, analyze, and use data efficiently.

Improved Decision-Making

Data is essential for making informed decisions, and databases provide a reliable source of data for analysis. By understanding databases, you can learn how to retrieve and analyze data to inform business decisions, identify trends, and gain insights.

Career Opportunities

In today's digital age, many career paths require knowledge of databases. By studying databases, you can open up new career opportunities in software development, data analysis, database administration and related fields.

Needless to say, studying databases is essential for anyone who deals with data. Whether you're looking to start a new career or enhance your existing skills, studying databases is a critical step towards success in today's data-driven world.

Final Takeaways

In conclusion, as you are interested in database technology, we hope this blog has given you some insights into the latest research topics in the field. From blockchain to AI, from sports to marketing, there are a plethora of exciting database topics for research papers that will shape the future of database technology.

As technology continues to evolve, it is essential to stay up-to-date with the latest trends in the field of databases. Our curated KnowledgeHut Database Certification Courses will help you stay ahead of the curve and develop new skills.

We hope this blog has inspired you to explore the exciting world of database research in 2024. Stay curious and keep learning!

Frequently Asked Questions (FAQs)

There are several examples of databases, with the five most common ones being:

MySQL : An open-source RDBMS used commonly in web applications.

Microsoft SQL Server : A popular RDBMS used in enterprise environments.

Oracle : A trusted commercial RDBMS famous for its high-scalability and security.

MongoDB : A NoSQL document-oriented database optimized for storing large amounts of unstructured data.

PostgreSQL : An open-source RDBMS offering advanced features like high concurrency and support for multiple data types.

Structured Query Language (SQL) is a high-level language designed to communicate with relational databases. It’s not a database in and of itself. Rather, it’s a language used to create, modify, and retrieve data from relational databases such as MySQL and Oracle.

A primary key is a column (or a set of columns) that uniquely identifies each row in a table. In technical terms, the primary key is a unique identifier of records. It’s used as a reference to establish relationships between various tables.

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Database Management Systems (DBMS)

Database group website: db.cs.berkeley.edu

Declarative languages and runtime systems

Design and implementation of declarative programming languages with applications to distributed systems, networking, machine learning, metadata management, and interactive visualization; design of query interface for applications.

Scalable data analysis and query processing

Scalable data processing in new settings, including interactive exploration, metadata management, cloud and serverless environments, and machine learning; query processing on compressed, semi-structured, and streaming data; query processing with additional constraints, including fairness, resource utilization, and cost.

Consistency, concurrency, coordination and reliability

Coordination avoidance, consistency and monotonicity analysis; transaction isolation levels and protocols; distributed analytics and data management, geo-replication; fault tolerance and fault injection.

Data storage and physical design

Hot and cold storage; immutable data structures; indexing and data skipping; versioning; new data types; implications of hardware evolution.

Metadata management

Data lineage and versioning; usage tracking and collective intelligence; scalability of metadata management services; metadata representations; reproducibility and debugging of data pipelines.

Systems for machine learning and model management

Distributed machine learning and graph analytics; physical and logical optimization of machine learning pipelines; online model management and maintenance; prediction serving; real-time personalization; latency-accuracy tradeoffs and edge computing for large-scale models; machine learning lifecycle management.

Data cleaning, data transformation, and crowdsourcing

Human-data interaction including interactive transformation, query authoring, and crowdsourcing; machine learning for data cleaning; statistical properties of data cleaning pipelines; end-to-end systems for crowdsourcing.

Interactive data exploration and visualization

Interactive querying and direct manipulation; scalable spreadsheets and data visualization; languages and interfaces for interactive exploration; progressive query visualization; predictive interaction.

Secure data processing

Data processing under homomorphic encryption; data compression and encryption; differential privacy; oblivious data processing; databases in secure hardware enclaves.

Foundations of data management

Optimal trade-offs between storage, quality, latency, and cost, with applications to crowdsourcing, distributed data management, stream data processing, version management; expressiveness, complexity, and completeness of data representations, query languages, and query processing; query processing with fairness constraints.

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Related Courses

• CS 186. Introduction to Database Systems
• CS 262A. Advanced Topics in Computer Systems

Research Topics & Ideas: Data Science

50 Topic Ideas To Kickstart Your Research Project

If you’re just starting out exploring data science-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of data science and analytics-related research ideas , including examples from recent studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .

Data Science-Related Research Topics

• Developing machine learning models for real-time fraud detection in online transactions.
• The use of big data analytics in predicting and managing urban traffic flow.
• Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
• The application of predictive analytics in personalizing cancer treatment plans.
• Analyzing consumer behavior through big data to enhance retail marketing strategies.
• The role of data science in optimizing renewable energy generation from wind farms.
• Developing natural language processing algorithms for real-time news aggregation and summarization.
• The application of big data in monitoring and predicting epidemic outbreaks.
• Investigating the use of machine learning in automating credit scoring for microfinance.
• The role of data analytics in improving patient care in telemedicine.
• Developing AI-driven models for predictive maintenance in the manufacturing industry.
• The use of big data analytics in enhancing cybersecurity threat intelligence.
• Investigating the impact of sentiment analysis on brand reputation management.
• The application of data science in optimizing logistics and supply chain operations.
• Developing deep learning techniques for image recognition in medical diagnostics.
• The role of big data in analyzing climate change impacts on agricultural productivity.
• Investigating the use of data analytics in optimizing energy consumption in smart buildings.
• The application of machine learning in detecting plagiarism in academic works.
• Analyzing social media data for trends in political opinion and electoral predictions.
• The role of big data in enhancing sports performance analytics.
• Developing data-driven strategies for effective water resource management.
• The use of big data in improving customer experience in the banking sector.
• Investigating the application of data science in fraud detection in insurance claims.
• The role of predictive analytics in financial market risk assessment.
• Developing AI models for early detection of network vulnerabilities.

Data Science Research Ideas (Continued)

• The application of big data in public transportation systems for route optimization.
• Investigating the impact of big data analytics on e-commerce recommendation systems.
• The use of data mining techniques in understanding consumer preferences in the entertainment industry.
• Developing predictive models for real estate pricing and market trends.
• The role of big data in tracking and managing environmental pollution.
• Investigating the use of data analytics in improving airline operational efficiency.
• The application of machine learning in optimizing pharmaceutical drug discovery.
• Analyzing online customer reviews to inform product development in the tech industry.
• The role of data science in crime prediction and prevention strategies.
• Developing models for analyzing financial time series data for investment strategies.
• The use of big data in assessing the impact of educational policies on student performance.
• Investigating the effectiveness of data visualization techniques in business reporting.
• The application of data analytics in human resource management and talent acquisition.
• Developing algorithms for anomaly detection in network traffic data.
• The role of machine learning in enhancing personalized online learning experiences.
• Investigating the use of big data in urban planning and smart city development.
• The application of predictive analytics in weather forecasting and disaster management.
• Analyzing consumer data to drive innovations in the automotive industry.
• The role of data science in optimizing content delivery networks for streaming services.
• Developing machine learning models for automated text classification in legal documents.
• The use of big data in tracking global supply chain disruptions.
• Investigating the application of data analytics in personalized nutrition and fitness.
• The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
• Developing predictive models for customer churn in the telecommunications industry.
• The application of data science in optimizing advertisement placement and reach.

Recent Data Science-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
• Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
• Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
• Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
• An Essay on How Data Science Can Strengthen Business (Santos, 2023)
• A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
• You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
• Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
• Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
• A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
• Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
• Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
• Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
• Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
• TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
• Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
• MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH (Mike & Hazzan, 2022)
• COMPONENTS OF DATA SCIENCE AND ITS APPLICATIONS (Paul et al., 2022)
• Analysis on the Application of Data Science in Business Analytics (Wang, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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Research Paper Help

• Types of information
• Refining your topic and identifying search terms
• Database searching
• Which databases should you try?
• Open Web and Statistics
• Misinformation and disinformation
• MLA Citation Guide
• Share Your Research!

Topics and search terms

• Video: Picking your topic IS research! (3 minutes) This video from NCSU highlights how the research process starts before you even figure out your topic. It provides an overview of one way to go about choosing a paper topic that is not only interesting to you, but also not too broad or narrow.
• Video: From Question to Keyword (1 minute) A quick visual narrative that shows how research topics can be turned into keyword searches, from PATH, Lighting Your Way from Research to Writing Tutorial, Module 2, University at North Carolina Greensboro University Libraries.
• Video: Search Smarter (4 minutes) This video from Oklahoma State University Low Library's "Inform Your Thinking" video series provides advice and context on how to target your searches based on your information need, and how to brainstorm good search terms for your topic.
• Boolean operators A fun graphic that demonstrates the difference between AND, OR, & NOT.
• Handout: Search Techniques "Cheat Sheet" A table with a series of tricks and strategies to use when searching in databases and on Google.

Quick Check: Topics and Database Searching

Sometimes the most difficult part of finding information that is relevant to your interests is finding the right search terms.  Look at this slideshow for a series of examples of research topics, and how those research topics might be turned into effective database searches . And remember, often your very best searches come after several tries -- you learn more about what search terms to use after you examine search results from less useful searches. Use database subject terms, abstracts, and open web Google searches to help find more effective terms and synonyms.

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Advances in database systems education: Methods, tools, curricula, and way forward

Muhammad ishaq.

1 Department of Computer Science, National University of Computer and Emerging Sciences, Lahore, Pakistan

2 Department of Computer Science, Virtual University of Pakistan, Lahore, Pakistan

3 Department of Computer Science, University of Management and Technology, Lahore, Pakistan

Muhammad Shoaib Farooq

Muhammad faraz manzoor.

4 Department of Computer Science, Lahore Garrison University, Lahore, Pakistan

Uzma Farooq

Kamran abid.

5 Department of Electrical Engineering, University of the Punjab, Lahore, Pakistan

Mamoun Abu Helou

6 Faculty of Information Technology, Al Istiqlal University, Jericho, Palestine

Associated Data

Not Applicable.

Fundamentals of Database Systems is a core course in computing disciplines as almost all small, medium, large, or enterprise systems essentially require data storage component. Database System Education (DSE) provides the foundation as well as advanced concepts in the area of data modeling and its implementation. The first course in DSE holds a pivotal role in developing students’ interest in this area. Over the years, the researchers have devised several different tools and methods to teach this course effectively, and have also been revisiting the curricula for database systems education. In this study a Systematic Literature Review (SLR) is presented that distills the existing literature pertaining to the DSE to discuss these three perspectives for the first course in database systems. Whereby, this SLR also discusses how the developed teaching and learning assistant tools, teaching and assessment methods and database curricula have evolved over the years due to rapid change in database technology. To this end, more than 65 articles related to DSE published between 1995 and 2022 have been shortlisted through a structured mechanism and have been reviewed to find the answers of the aforementioned objectives. The article also provides useful guidelines to the instructors, and discusses ideas to extend this research from several perspectives. To the best of our knowledge, this is the first research work that presents a broader review about the research conducted in the area of DSE.

Introduction

Database systems play a pivotal role in the successful implementation of the information systems to ensure the smooth running of many different organizations and companies (Etemad & Küpçü, 2018 ; Morien, 2006 ). Therefore, at least one course about the fundamentals of database systems is taught in every computing and information systems degree (Nagataki et al., 2013 ). Database System Education (DSE) is concerned with different aspects of data management while developing software (Park et al., 2017 ). The IEEE/ACM computing curricula guidelines endorse 30–50 dedicated hours for teaching fundamentals of design and implementation of database systems so as to build a very strong theoretical and practical understanding of the DSE topics (Cvetanovic et al., 2010 ).

Practically, most of the universities offer one user-oriented course at undergraduate level that covers topics related to the data modeling and design, querying, and a limited number of hours on theory (Conklin & Heinrichs, 2005 ; Robbert & Ricardo, 2003 ), where it is often debatable whether to utilize a design-first or query-first approach. Furthermore, in order to update the course contents, some recent trends, including big data and the notion of NoSQL should also be introduced in this basic course (Dietrich et al., 2008 ; Garcia-Molina, 2008 ). Whereas, the graduate course is more theoretical and includes topics related to DB architecture, transactions, concurrency, reliability, distribution, parallelism, replication, query optimization, along with some specialized classes.

Researchers have designed a variety of tools for making different concepts of introductory database course more interesting and easier to teach and learn interactively (Brusilovsky et al., 2010 ) either using visual support (Nagataki et al., 2013 ), or with the help of gamification (Fisher & Khine, 2006 ). Similarly, the instructors have been improvising different methods to teach (Abid et al., 2015 ; Domínguez & Jaime, 2010 ) and evaluate (Kawash et al., 2020 ) this theoretical and practical course. Also, the emerging and hot topics such as cloud computing and big data has also created the need to revise the curriculum and methods to teach DSE (Manzoor et al., 2020 ).

The research in database systems education has evolved over the years with respect to modern contents influenced by technological advancements, supportive tools to engage the learners for better learning, and improvisations in teaching and assessment methods. Particularly, in recent years there is a shift from self-describing data-driven systems to a problem-driven paradigm that is the bottom-up approach where data exists before being designed. This mainly relies on scientific, quantitative, and empirical methods for building models, while pushing the boundaries of typical data management by involving mathematics, statistics, data mining, and machine learning, thus opening a multidisciplinary perspective. Hence, it is important to devote a few lectures to introducing the relevance of such advance topics.

Researchers have provided useful review articles on other areas including Introductory Programming Language (Mehmood et al., 2020 ), use of gamification (Obaid et al., 2020 ), research trends in the use of enterprise service bus (Aziz et al., 2020 ), and the role of IoT in agriculture (Farooq et al., 2019 , 2020 ) However, to the best of our knowledge, no such study was found in the area of database systems education. Therefore, this study discusses research work published in different areas of database systems education involving curricula, tools, and approaches that have been proposed to teach an introductory course on database systems in an effective manner. The rest of the article has been structured in the following manner: Sect.  2 presents related work and provides a comparison of the related surveys with this study. Section  3 presents the research methodology for this study. Section  4 analyses the major findings of the literature reviewed in this research and categorizes it into different important aspects. Section  5 represents advices for the instructors and future directions. Lastly, Sect.  6 concludes the article.

Related work

Systematic Literature Reviews have been found to be a very useful artifact for covering and understanding a domain. A number of interesting review studies have been found in different fields (Farooq et al., 2021 ; Ishaq et al., 2021 ). Review articles are generally categorized into narrative or traditional reviews (Abid et al., 2016 ; Ramzan et al., 2019 ), systematic literature review (Naeem et al., 2020 ) and meta reviews or mapping study (Aria & Cuccurullo, 2017 ; Cobo et al., 2012 ; Tehseen et al., 2020 ). This study presents a systematic literature review on database system education.

The database systems education has been discussed from many different perspectives which include teaching and learning methods, curriculum development, and the facilitation of instructors and students by developing different tools. For instance, a number of research articles have been published focusing on developing tools for teaching database systems course (Abut & Ozturk, 1997 ; Connolly et al., 2005 ; Pahl et al., 2004 ). Furthermore, few authors have evaluated the DSE tools by conducting surveys and performing empirical experiments so as to gauge the effectiveness of these tools and their degree of acceptance among important stakeholders, teachers and students (Brusilovsky et al., 2010 ; Nelson & Fatimazahra, 2010 ). On the other hand, some case studies have also been discussed to evaluate the effectiveness of the improvised approaches and developed tools. For example, Regueras et al. ( 2007 ) presented a case study using the QUEST system, in which e-learning strategies are used to teach the database course at undergraduate level, while, Myers and Skinner ( 1997 ) identified the conflicts that arise when theories in text books regarding the development of databases do not work on specific applications.

Another important facet of DSE research focuses on the curriculum design and evolution for database systems, whereby (Alrumaih, 2016 ; Bhogal et al., 2012 ; Cvetanovic et al., 2010 ; Sahami et al., 2011 ) have proposed solutions for improvements in database curriculum for the better understanding of DSE among the students, while also keeping the evolving technology into the perspective. Similarly, Mingyu et al. ( 2017 ) have shared their experience in reforming the DSE curriculum by adding topics related to Big Data. A few authors have also developed and evaluated different tools to help the instructors teaching DSE.

There are further studies which focus on different aspects including specialized tools for specific topics in DSE (Mcintyre et al, 1995 ; Nelson & Fatimazahra, 2010 ). For instance, Mcintyre et al. ( 1995 ) conducted a survey about using state of the art software tools to teach advanced relational database design courses at Cleveland State University. However, the authors did not discuss the DSE curricula and pedagogy in their study. Similarly, a review has been conducted by Nelson and Fatimazahra ( 2010 ) to highlight the fact that the understanding of basic knowledge of database is important for students of the computer science domain as well as those belonging to other domains. They highlighted the issues encountered while teaching the database course in universities and suggested the instructors investigate these difficulties so as to make this course more effective for the students. Although authors have discussed and analyzed the tools to teach database, the tools are yet to be categorized according to different methods and research types within DSE. There also exists an interesting systematic mapping study by Taipalus and Seppänen ( 2020 ) that focuses on teaching SQL which is a specific topic of DSE. Whereby, they categorized the selected primary studies into six categories based on their research types. They utilized directed content analysis, such as, student errors in query formulation, characteristics and presentation of the exercise database, specific or non-specific teaching approach suggestions, patterns and visualization, and easing teacher workload.

Another relevant study that focuses on collaborative learning techniques to teach the database course has been conducted by Martin et al. ( 2013 ) This research discusses collaborative learning techniques and adapted it for the introductory database course at the Barcelona School of Informatics. The motive of the authors was to introduce active learning methods to improve learning and encourage the acquisition of competence. However, the focus of the study was only on a few methods for teaching the course of database systems, while other important perspectives, including database curricula, and tools for teaching DSE were not discussed in this study.

The above discussion shows that a considerable amount of research work has been conducted in the field of DSE to propose various teaching methods; develop and test different supportive tools, techniques, and strategies; and to improve the curricula for DSE. However, to the best of our knowledge, there is no study that puts all these relevant and pertinent aspects together while also classifying and discussing the supporting methods, and techniques. This review is considerably different from previous studies. Table ​ Table1 1 highlights the differences between this study and other relevant studies in the field of DSE using ✓ and – symbol reflecting "included" and "not included" respectively. Therefore, this study aims to conduct a systematic mapping study on DSE that focuses on compiling, classifying, and discussing the existing work related to pedagogy, supporting tools, and curricula.

Comparison with other related research articles

Research methodology

In order to preserve the principal aim of this study, which is to review the research conducted in the area of database systems education, a piece of advice has been collected from existing methods described in various studies (Elberzhager et al., 2012 ; Keele et al., 2007 ; Mushtaq et al., 2017 ) to search for the relevant papers. Thus, proper research objectives were formulated, and based on them appropriate research questions and search strategy were formulated as shown in Fig.  1 .

Research objectives

The Following are the research objectives of this study:

• i. To find high quality research work in DSE.
• ii. To categorize different aspects of DSE covered by other researchers in the field.
• iii. To provide a thorough discussion of the existing work in this study to provide useful information in the form of evolution, teaching guidelines, and future research directions of the instructors.

Research questions

In order to fulfill the research objectives, some relevant research questions have been formulated. These questions along with their motivations have been presented in Table ​ Table2 2 .

Study selection results

Search strategy

The Following search string used to find relevant articles to conduct this study. “Database” AND (“System” OR “Management”) AND (“Education*” OR “Train*” OR “Tech*” OR “Learn*” OR “Guide*” OR “Curricul*”).

Articles have been taken from different sources i.e. IEEE, Springer, ACM, Science Direct and other well-known journals and conferences such as Wiley Online Library, PLOS and ArXiv. The planning for search to find the primary study in the field of DSE is a vital task.

Study selection

A total of 29,370 initial studies were found. These articles went through a selection process, and two authors were designated to shortlist the articles based on the defined inclusion criteria as shown in Fig.  2 . Their conflicts were resolved by involving a third author; while the inclusion/exclusion criteria were also refined after resolving the conflicts as shown in Table ​ Table3. 3 . Cohen’s Kappa coefficient 0.89 was observed between the two authors who selected the articles, which reflects almost perfect agreement between them (Landis & Koch, 1977 ). While, the number of papers in different stages of the selection process for all involved portals has been presented in Table ​ Table4 4 .

Selection criteria

Title based search: Papers that are irrelevant based on their title are manually excluded in the first stage. At this stage, there was a large portion of irrelevant papers. Only 609 papers remained after this stage.

Abstract based search: At this stage, abstracts of the selected papers in the previous stage are studied and the papers are categorized for the analysis along with research approach. After this stage only 152 papers were left.

Full text based analysis: Empirical quality of the selected articles in the previous stage is evaluated at this stage. The analysis of full text of the article has been conducted. The total of 70 papers were extracted from 152 papers for primary study. Following questions are defined for the conduction of final data extraction.

Quality assessment criteria

Following are the criteria used to assess the quality of the selected primary studies. This quality assessment was conducted by two authors as explained above.

• The study focuses on curricula, tools, approach, or assessments in DSE, the possible answers were Yes (1), No (0)
• The study presents a solution to the problem in DSE, the possible answers to this question were Yes (1), Partially (0.5), No (0)
• The study focuses on empirical results, Yes (1), No (0)

Score pattern of publication channels

Almost 50.00% of papers had scored more than average and 33.33% of papers had scored between the average range i.e., 2.50–3.50. Some articles with the score below 2.50 have also been included in this study as they present some useful information and were published in education-based journals. Also, these studies discuss important demography and technology based aspects that are directly related to DSE.

Threats to validity

The validity of this study could be influenced by the following factors during the literature of this publication.

Construct validity

In this study this validity identifies the primary study for research (Elberzhager et al., 2012 ). To ensure that many primary studies have been included in this literature two authors have proposed possible search keywords in multiple repetitions. Search string is comprised of different terms related to DS and education. Though, list might be incomplete, count of final papers found can be changed by the alternative terms (Ampatzoglou et al., 2013 ). IEEE digital library, Science direct, ACM digital library, Wiley Online Library, PLOS, ArXiv and Google scholar are the main libraries where search is done. We believe according to the statistics of search engines of literature the most research can be found on these digital libraries (Garousi et al., 2013 ). Researchers also searched related papers in main DS research sites (VLDB, ICDM, EDBT) in order to minimize the risk of missing important publication.

Including the papers that does not belong to top journals or conferences may reduce the quality of primary studies in this research but it indicates that the representativeness of the primary studies is improved. However, certain papers which were not from the top publication sources are included because of their relativeness wisth the literature, even though they reduce the average score for primary studies. It also reduces the possibility of alteration of results which might have caused by the improper handling of duplicate papers. Some cases of duplications were found which were inspected later whether they were the same study or not. The two authors who have conducted the search has taken the final decision to the select the papers. If there is no agreement between then there must be discussion until an agreement is reached.

Internal validity

This validity deals with extraction and data analysis (Elberzhager et al., 2012 ). Two authors carried out the data extraction and primary studies classification. While the conflicts between them were resolved by involving a third author. The Kappa coefficient was 0.89, according to Landis and Koch ( 1977 ), this value indicates almost perfect level of agreement between the authors that reduces this threat significantly.

Conclusion validity

This threat deals with the identification of improper results which may cause the improper conclusions. In this case this threat deals with the factors like missing studies and wrong data extraction (Ampatzoglou et al., 2013 ). The objective of this is to limit these factors so that other authors can perform study and produce the proper conclusions (Elberzhager et al., 2012 ).

Interpretation of results might be affected by the selection and classification of primary studies and analyzing the selected study. Previous section has clearly described each step performed in primary study selection and data extraction activity to minimize this threat. The traceability between the result and data extracted was supported through the different charts. In our point of view, slight difference based on the publication selection and misclassification would not alter the main results.

External validity

This threat deals with the simplification of this research (Mateo et al., 2012 ). The results of this study were only considered that related to the DSE filed and validation of the conclusions extracted from this study only concerns the DSE context. The selected study representativeness was not affected because there was no restriction on time to find the published research. Therefore, this external validity threat is not valid in the context of this research. DS researchers can take search string and the paper classification scheme represented in this study as an initial point and more papers can be searched and categorized according to this scheme.

Analysis of compiled research articles

This section presents the analysis of the compiled research articles carefully selected for this study. It presents the findings with respect to the research questions described in Table ​ Table2 2 .

Selection results

A total of 70 papers were identified and analyzed for the answers of RQs described above. Table ​ Table6 6 represents a list of the nominated papers with detail of the classification results and their quality assessment scores.

Classification and quality assessment of selected articles

RQ1.Categorization of research work in DSE field

The analysis in this study reveals that the literature can be categorized as: Tools: any additional application that helps instructors in teaching and students in learning. Methods: any improvisation aimed at improving pedagogy or cognition. Curriculum: refers to the course content domains and their relative importance in a degree program, as shown in Fig.  3 .

Taxonomy of DSE study types

Most of the articles provide a solution by gathering the data and also prove the novelty of their research through results. These papers are categorized as experiments w.r.t. their research types. Whereas, some of them case study papers which are used to generate an in depth, multifaceted understanding of a complex issue in its real-life context, while few others are review studies analyzing the previously used approaches. On the other hand, a majority of included articles have evaluated their results with the help of experiments, while others conducted reviews to establish an opinion as shown in Fig.  4 .

Cross Mapping of DSE study type and research Types

Educational tools, especially those related to technology, are making their place in market faster than ever before (Calderon et al., 2011 ). The transition to active learning approaches, with the learner more engaged in the process rather than passively taking in information, necessitates a variety of tools to help ensure success. As with most educational initiatives, time should be taken to consider the goals of the activity, the type of learners, and the tools needed to meet the goals. Constant reassessment of tools is important to discover innovation and reforms that improve teaching and learning (Irby & Wilkerson, 2003 ). For this purpose, various type of educational tools such as, interactive, web-based and game based have been introduced to aid the instructors in order to explain the topic in more effective way.

The inclusion of technology into the classroom may help learners to compete in the competitive market when approaching the start of their career. It is important for the instructors to acknowledge that the students are more interested in using technology to learn database course instead of merely being taught traditional theory, project, and practice-based methods of teaching (Adams et al., 2004 ). Keeping these aspects in view many authors have done significant research which includes web-based and interactive tools to help the learners gain better understanding of basic database concepts.

Great research has been conducted with the focus of students learning. In this study we have discussed the students learning supportive with two major finding’s objectives i.e., tools which prove to be more helpful than other tools. Whereas, proposed tools with same outcome as traditional classroom environment. Such as, Abut and Ozturk ( 1997 ) proposed an interactive classroom environment to conduct database classes. The online tools such as electronic “Whiteboard”, electronic textbooks, advance telecommunication networks and few other resources such as Matlab and World Wide Web were the main highlights of their proposed smart classroom. Also, Pahl et al. ( 2004 ) presented an interactive multimedia-based system for the knowledge and skill oriented Web-based education of database course students. The authors had differentiated their proposed classroom environment from traditional classroom-based approach by using tool mediated independent learning and training in an authentic setting. On the other hand, some authors have also evaluated the educational tools based on their usage and impact on students’ learning. For example, Brusilovsky et al. ( 2010 )s evaluated the technical and conceptual difficulties of using several interactive educational tools in the context of a single course. A combined Exploratorium has been presented for database courses and an experimental platform, which delivers modified access to numerous types of interactive learning activities.

Also, Taipalus and Perälä ( 2019 ) investigated the types of errors that are persistent in writing SQL by the students. The authors also contemplated the errors while mapping them onto different query concepts. Moreover, Abelló Gamazo et al. ( 2016 ) presented a software tool for the e-assessment of relational database skills named LearnSQL. The proposed software allows the automatic and efficient e-learning and e-assessment of relational database skills. Apart from these, Yue ( 2013 ) proposed the database tool named Sakila as a unified platform to support instructions and multiple assignments of a graduate database course for five semesters. According to this study, students find this tool more useful and interesting than the highly simplified databases developed by the instructor, or obtained from textbook. On the other hand, authors have proposed tools with the main objective to help the student’s grip on the topic by addressing the pedagogical problems in using the educational tools. Connolly et al. ( 2005 ) discussed some of the pedagogical problems sustaining the development of a constructive learning environment using problem-based learning, a simulation game and interactive visualizations to help teach database analysis and design. Also, Yau and Karim ( 2003 ) proposed smart classroom with prevalent computing technology which will facilitate collaborative learning among the learners. The major aim of this smart classroom is to improve the quality of interaction between the instructors and students during lecture.

Student satisfaction is also an important factor for the educational tools to more effective. While it supports in students learning process it should also be flexible to achieve the student’s confidence by making it as per student’s needs (Brusilovsky et al., 2010 ; Connolly et al., 2005 ; Pahl et al., 2004 ). Also, Cvetanovic et al. ( 2010 ) has proposed a web-based educational system named ADVICE. The proposed solution helps the students to reduce the gap between DBMS, theory and its practice. On the other hand, authors have enhanced the already existing educational tools in the traditional classroom environment to addressed the student’s concerns (Nelson & Fatimazahra, 2010 ; Regueras et al., 2007 ) Table ​ Table7 7 .

Tools: Adopted in DSE and their impacts

Hands on database development is the main concern in most of the institute as well as in industry. However, tools assisting the students in database development and query writing is still major concern especially in SQL (Brusilovsky et al., 2010 ; Nagataki et al., 2013 ).

Student’s grades reflect their conceptual clarity and database development skills. They are also important to secure jobs and scholarships after passing out, which is why it is important to have the educational learning tools to help the students to perform well in the exams (Cvetanovic et al., 2010 ; Taipalus et al., 2018 ). While, few authors (Wang et al., 2010 ) proposed Metube which is a variation of YouTube. Subsequently, existing educational tools needs to be upgraded or replaced by the more suitable assessment oriented interactive tools to attend challenging students needs (Pahl et al., 2004 ; Yuelan et al., 2011 ).

One other objective of developing the educational tools is to increase the interaction between the students and the instructors. In the modern era, almost every institute follows the student centered learning(SCL). In SCL the interaction between students and instructor increases with most of the interaction involves from the students. In order to support SCL the educational based interactive and web-based tools need to assign more roles to students than the instructors (Abbasi et al., 2016 ; Taipalus & Perälä, 2019 ; Yau & Karim, 2003 ).

Theory versus practice is still one of the main issues in DSE teaching methods. The traditional teaching method supports theory first and then the concepts learned in the theoretical lectures implemented in the lab. Whereas, others think that it is better to start by teaching how to write query, which should be followed by teaching the design principles for database, while a limited amount of credit hours are also allocated for the general database theory topics. This part of the article discusses different trends of teaching and learning style along with curriculum and assessments methods discussed in DSE literature.

A variety of teaching methods have been designed, experimented, and evaluated by different researchers (Yuelan et al., 2011 ; Chen et al., 2012 ; Connolly & Begg, 2006 ). Some authors have reformed teaching methods based on the requirements of modern way of delivering lectures such as Yuelan et al. ( 2011 ) reform teaching method by using various approaches e.g. a) Modern ways of education: includes multimedia sound, animation, and simulating the process and working of database systems to motivate and inspire the students. b) Project driven approach: aims to make the students familiar with system operations by implementing a project. c) Strengthening the experimental aspects: to help the students get a strong grip on the basic knowledge of database and also enable them to adopt a self-learning ability. d) Improving the traditional assessment method: the students should turn in their research and development work as the content of the exam, so that they can solve their problem on their own.

The main aim of any teaching method is to make student learn the subject effectively. Student must show interest in order to gain something from the lectures delivered by the instructors. For this, teaching methods should be interactive and interesting enough to develop the interest of the students in the subject. Students can show interest in the subject by asking more relative questions or completing the home task and assignments on time. Authors have proposed few teaching methods to make topic more interesting such as, Chen et al. ( 2012 ) proposed a scaffold concept mapping strategy, which considers a student’s prior knowledge, and provides flexible learning aids (scaffolding and fading) for reading and drawing concept maps. Also, Connolly & Begg (200s6) examined different problems in database analysis and design teaching, and proposed a teaching approach driven by principles found in the constructivist epistemology to overcome these problems. This constructivist approach is based on the cognitive apprenticeship model and project-based learning. Similarly, Domínguez & Jaime ( 2010 ) proposed an active method for database design through practical tasks development in a face-to-face course. They analyzed results of five academic years using quasi experimental. The first three years a traditional strategy was followed and a course management system was used as material repository. On the other hand, Dietrich and Urban ( 1996 ) have described the use of cooperative group learning concepts in support of an undergraduate database management course. They have designed the project deliverables in such a way that students develop skills for database implementation. Similarly, Zhang et al. ( 2018 ) have discussed several effective classroom teaching measures from the aspects of the innovation of teaching content, teaching methods, teaching evaluation and assessment methods. They have practiced the various teaching measures by implementing the database technologies and applications in Qinghai University. Moreover, Hou and Chen ( 2010 ) proposed a new teaching method based on blending learning theory, which merges traditional and constructivist methods. They adopted the method by applying the blending learning theory on Access Database programming course teaching.

Problem solving skills is a key aspect to any type of learning at any age. Student must possess this skill to tackle the hurdles in institute and also in industry. Create mind and innovative students find various and unique ways to solve the daily task which is why they are more likeable to secure good grades and jobs. Authors have been working to introduce teaching methods to develop problem solving skills in the students(Al-Shuaily, 2012 ; Cai & Gao, 2019 ; Martinez-González & Duffing, 2007 ; Gudivada et al., 2007 ). For instance, Al-Shuaily ( 2012 ) has explored four cognitive factors such as i) Novices’ ability in understanding, ii) Novices’ ability to translate, iii) Novice’s ability to write, iv) Novices’ skills that might influence SQL teaching, and learning methods and approaches. Also, Cai and Gao ( 2019 ) have reformed the teaching method in the database course of two higher education institutes in China. Skills and knowledge, innovation ability, and data abstraction were the main objective of their study. Similarly, Martinez-González and Duffing ( 2007 ) analyzed the impact of convergence of European Union (EU) in different universities across Europe. According to their study, these institutes need to restructure their degree program and teaching methodologies. Moreover, Gudivada et al. ( 2007 ) proposed a student’s learning method to work with the large datasets. they have used the Amazon Web Services API and.NET/C# application to extract a subset of the product database to enhance student learning in a relational database course.

On the other hand, authors have also evaluated the traditional teaching methods to enhance the problem-solving skills among the students(Eaglestone & Nunes, 2004 ; Wang & Chen, 2014 ; Efendiouglu & Yelken, 2010 ) Such as, Eaglestone and Nunes ( 2004 ) shared their experiences of delivering a database design course at Sheffield University and discussed some of the issues they faced, regarding teaching, learning and assessments. Likewise, Wang and Chen ( 2014 ) summarized the problems mainly in teaching of the traditional database theory and application. According to the authors the teaching method is outdated and does not focus on the important combination of theory and practice. Moreover, Efendiouglu and Yelken ( 2010 ) investigated the effects of two different methods Programmed Instruction (PI) and Meaningful Learning (ML) on primary school teacher candidates’ academic achievements and attitudes toward computer-based education, and to define their views on these methods. The results show that PI is not favoured for teaching applications because of its behavioural structure Table ​ Table8 8 .

Methods: Teaching approaches adopted in DSE

Students become creative and innovative when the try to study on their own and also from different resources rather than curriculum books only. In the modern era, there are various resources available on both online and offline platforms. Modern teaching methods must emphasize on making the students independent from the curriculum books and educate them to learn independently(Amadio et al., 2003 ; Cai & Gao, 2019 ; Martin et al., 2013 ). Also, in the work of Kawash et al. ( 2020 ) proposed he group study-based learning approach called Graded Group Activities (GGAs). In this method students team up in order to take the exam as a group. On the other hand, few studies have emphasized on course content to prepare students for the final exams such as, Zheng and Dong ( 2011 ) have discussed the issues of computer science teaching with particular focus on database systems, where different characteristics of the course, teaching content and suggestions to teach this course effectively have been presented.

As technology is evolving at rapid speed, so students need to have practical experience from the start. Basic theoretical concepts of database are important but they are of no use without its implementation in real world projects. Most of the students study in the institutes with the aim of only clearing the exams with the help of theoretical knowledge and very few students want to have practical experience(Wang & Chen, 2014 ; Zheng & Dong, 2011 ). To reduce the gap between the theory and its implementation, authors have proposed teaching methods to develop the student’s interest in the real-world projects (Naik & Gajjar, 2021 ; Svahnberg et al., 2008 ; Taipalus et al., 2018 ). Moreover, Juxiang and Zhihong ( 2012 ) have proposed that the teaching organization starts from application scenarios, and associate database theoretical knowledge with the process from analysis, modeling to establishing database application. Also, Svahnberg et al. ( 2008 ) explained that in particular conditions, there is a possibility to use students as subjects for experimental studies in DSE and influencing them by providing responses that are in line with industrial practice.

On the other hand, Nelson et al. ( 2003 ) evaluated the different teaching methods used to teach different modules of database in the School of Computing and Technology at the University of Sunder- land. They outlined suggestions for changes to the database curriculum to further integrate research and state-of-the-art systems in databases.

• III. Curriculum

Database curriculum has been revisited many times in the form of guidelines that not only present the contents but also suggest approximate time to cover different topics. According to the ACM curriculum guidelines (Lunt et al., 2008 ) for the undergraduate programs in computer science, the overall coverage time for this course is 46.50 h distributed in such a way that 11 h is the total coverage time for the core topics such as, Information Models (4 core hours), Database Systems (3 core hours) and Data Modeling (4 course hours). Whereas, the remaining hours are allocated for elective topics such as Indexing, Relational Databases, Query Languages, Relational Database Design, Transaction Processing, Distributed Databases, Physical Database Design, Data Mining, Information Storage and Retrieval, Hypermedia, Multimedia Systems, and Digital Libraries(Marshall, 2012 ). While, according to the ACM curriculum guidelines ( 2013 ) for undergraduate programs in computer science, this course should be completed in 15 weeks with two and half hour lecture per week and lab session of four hours per week on average (Brady et al., 2004 ). Thus, the revised version emphasizes on the practice based learning with the help of lab component. Numerous organizations have exerted efforts in this field to classify DSE (Dietrich et al., 2008 ). DSE model curricula, bodies of knowledge (BOKs), and some standardization aspects in this field are discussed below:

Model curricula

There are standard bodies who set the curriculum guidelines for teaching undergraduate degree programs in computing disciplines. Curricula which include the guidelines to teach database are: Computer Engineering Curricula (CEC) (Meier et al., 2008 ), Information Technology Curricula (ITC) (Alrumaih, 2016 ), Computing Curriculum Software Engineering (CCSE) (Meyer, 2001 ), Cyber Security Curricula (CSC) (Brady et al., 2004 ; Bishop et al., 2017 ).

Bodies of knowledge (BOK)

A BOK includes the set of thoughts and activities related to the professional area, while in model curriculum set of guidelines are given to address the education issues (Sahami et al., 2011 ). Database body of Knowledge comprises of (a) The Data Management Body of Knowledge (DM- BOK), (b) Software Engineering Education Knowledge (SEEK) (Sobel, 2003 ) (Sobel, 2003 ), and (c) The SE body of knowledge (SWEBOK) (Swebok Evolution: IEEE Computer Society n.d. ).

Apart from the model curricula, and bodies of knowledge, there also exist some standards related to the database and its different modules: ISO/IEC 9075–1:2016 (Computing Curricula, 1991 ), ISO/IEC 10,026–1: 1998 (Suryn, 2003 ).

We also utilize advices from some studies (Elberzhager et al., 2012 ; Keele et al., 2007 ) to search for relevant papers. In order to conduct this systematic study, it is essential to formulate the primary research questions (Mushtaq et al., 2017 ). Since the data management techniques and software are evolving rapidly, the database curriculum should also be updated accordingly to meet these new requirements. Some authors have described ways of updating the content of courses to keep pace with specific developments in the field and others have developed new database curricula to keep up with the new data management techniques.

Furthermore, some authors have suggested updates for the database curriculum based on the continuously evolving technology and introduction of big data. For instance Bhogal et al. ( 2012 ) have shown that database curricula need to be updated and modernized, which can be achieved by extending the current database concepts that cover the strategies to handle the ever changing user requirements and how database technology has evolved to meet the requirements. Likewise, Picciano ( 2012 ) examines the evolving world of big data and analytics in American higher education. According to the author, the “data driven” decision making method should be used to help the institutes evaluate strategies that can improve retention and update the curriculum that has big data basic concepts and applications, since data driven decision making has already entered in the big data and learning analytic era. Furthermore, Marshall ( 2011 ) presented the challenges faced when developing a curriculum for a Computer Science degree program in the South African context that is earmarked for international recognition. According to the author, the Curricula needs to adhere both to the policy and content requirements in order to be rated as being of a particular quality.

Similarly, some studies (Abourezq & Idrissi, 2016 ; Mingyu et al., 2017 ) described big data influence from a social perspective and also proceeded with the gaps in database curriculum of computer science, especially, in the big data era and discovers the teaching improvements in practical and theoretical teaching mode, teaching content and teaching practice platform in database curriculum. Also Silva et al. ( 2016 ) propose teaching SQL as a general language that can be used in a wide range of database systems from traditional relational database management systems to big data systems.

On the other hand, different authors have developed a database curriculum based on the different academic background of students. Such as, Dean and Milani ( 1995 ) have recommended changes in computer science curricula based on the practice in United Stated Military Academy (USMA). They emphasized greatly on the practical demonstration of the topic rather than the theoretical explanation. Especially, for the non-computer science major students. Furthermore, Urban and Dietrich ( 2001 ) described the development of a second course on database systems for undergraduates, preparing students for the advanced database concepts that they will exercise in the industry. They also shared their experience with teaching the course, elaborating on the topics and assignments. Also, Andersson et al. ( 2019 ) proposed variations in core topics of database management course for the students with the engineering background. Moreover, Dietrich et al. ( 2014 ) described two animations developed with images and color that visually and dynamically introduce fundamental relational database concepts and querying to students of many majors. The goal is that the educators, in diverse academic disciplines, should be able to incorporate these animations in their existing courses to meet their pedagogical needs.

The information systems have evolved into large scale distributed systems that store and process a huge amount of data across different servers, and process them using different distributed data processing frameworks. This evolution has given birth to new paradigms in database systems domain termed as NoSQL and Big Data systems, which significantly deviate from conventional relational and distributed database management systems. It is pertinent to mention that in order to offer a sustainable and practical CS education, these new paradigms and methodologies as shown in Fig.  5 should be included into database education (Kleiner, 2015 ). Tables ​ Tables9 9 and ​ and10 10 shows the summarized findings of the curriculum based reviewed studies. This section also proposed appropriate text book based on the theory, project, and practice-based teaching methodology as shown in Table ​ Table9. 9 . The proposed books are selected purely on the bases of their usage in top universities around the world such as, Massachusetts Institute of Technology, Stanford University, Harvard University, University of Oxford, University of Cambridge and, University of Singapore and the coverage of core topics mentioned in the database curriculum.

Concepts in Database Systems Education (Kleiner, 2015 )

Recommended text books for DSE

Curriculum: Findings of Reviewed Literature

RQ.2 Evolution of DSE research

This section discusses the evolution of database while focusing the DSE over the past 25 years as shown in Fig.  6 .

Evolution of DSE studies

This study shows that there is significant increase in research in DSE after 2004 with 78% of the selected papers are published after 2004. The main reason of this outcome is that some of the papers are published in well-recognized channels like IEEE Transactions on Education, ACM Transactions on Computing Education, International Conference on Computer Science and Education (ICCSE), and Teaching, Learning and Assessment of Database (TLAD) workshop. It is also evident that several of these papers were published before 2004 and only a few articles were published during late 1990s. This is because of the fact that DSE started to gain interest after the introduction of Body of Knowledge and DSE standards. The data intensive scientific discovery has been discussed as the fourth paradigm (Hey et al., 2009 ): where the first involves empirical science and observations; second contains theoretical science and mathematically driven insights; third considers computational science and simulation driven insights; while the fourth involves data driven insights of modern scientific research.

Over the past few decades, students have gone from attending one-room class to having the world at their fingertips, and it is a great challenge for the instructors to develop the interest of students in learning database. This challenge has led to the development of the different types of interactive tools to help the instructors teach DSE in this technology oriented era. Keeping the importance of interactive tools in DSE in perspective, various authors have proposed different interactive tools over the years, such as during 1995–2003, when different authors proposed various interactive tools. Some studies (Abut & Ozturk, 1997 ; Mcintyre et al., 1995 ) introduced state of the art interactive tools to teach and enhance the collaborative learning among the students. Similarly, during 2004–2005 more interactive tools in the field of DSE were proposed such as Pahl et al. ( 2004 ), Connolly et al. ( 2005 ) introduced multimedia system based interactive model and game based collaborative learning environment.

The Internet has started to become more common in the first decade of the twenty-first century and its positive impact on the education sector was undeniable. Cost effective, student teacher peer interaction, keeping in touch with the latest information were the main reasons which made the instructors employ web-based tools to teach database in the education sector. Due to this spike in the demand of web-based tools, authors also started to introduce new instruments to assist with teaching database. In 2007 Regueras et al. ( 2007 ) proposed an e-learning tool named QUEST with a feedback module to help the students to learn from their mistakes. Similarly, in 2010, multiple authors have proposed and evaluated various web-based tools. Cvetanovic et al. ( 2010 ) proposed ADVICE with the functionality to monitor student’s progress, while, few authors (Wang et al., 2010 ) proposed Metube which is a variation of YouTube. Furthermore, Nelson and Fatimazahra ( 2010 ) evaluated different web-based tools to highlight the complexities of using these web-based instruments.

Technology has changed the teaching methods in the education sector but technology cannot replace teachers, and despite the amount of time most students spend online, virtual learning will never recreate the teacher-student bond. In the modern era, innovation in technology used in educational sectors is not meant to replace the instructors or teaching methods.

During the 1990s some studies (Dietrich & Urban, 1996 ; Urban & Dietrich, 1997 ) proposed learning and teaching methods respectively keeping the evolving technology in view. The highlight of their work was project deliverables and assignments where students progressively advanced to a step-by-step extension, from a tutorial exercise and then attempting more difficult extension of assignment.

During 2002–2007 various authors have discussed a number of teaching and learning methods to keep up the pace with the ever changing database technology, such as Connolly and Begg ( 2006 ) proposing a constructive approach to teach database analysis and design. Similarly, Prince and Felder ( 2006 ) reviewed the effectiveness of inquiry learning, problem based learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching. Also, McIntyre et al. (Mcintyre et al., 1995 ) brought to light the impact of convergence of European Union (EU) in different universities across Europe. They suggested a reconstruction of teaching and learning methodologies in order to effectively teach database.

During 2008–2013 more work had been done to address the different methods of teaching and learning in the field of DSE, like the work of Dominguez and Jaime ( 2010 ) who proposed an active learning approach. The focus of their study was to develop the interest of students in designing and developing databases. Also, Zheng and Dong ( 2011 ) have highlighted various characteristics of the database course and its teaching content. Similarly, Yuelan et al. ( 2011 ) have reformed database teaching methods. The main focus of their study were the Modern ways of education, project driven approach, strengthening the experimental aspects, and improving the traditional assessment method. Likewise, Al-Shuaily ( 2012 ) has explored 4 cognitive factors that can affect the learning process of database. The main focus of their study was to facilitate the students in learning SQL. Subsequently, Chen et al. ( 2012 ) also proposed scaffolding-based concept mapping strategy. This strategy helps the students to better understand database management courses. Correspondingly, Martin et al. ( 2013 ) discussed various collaborative learning techniques in the field of DSE while keeping database as an introductory course.

In the years between 2014 and 2021, research in the field of DSE increased, which was the main reason that the most of teaching, learning and assessment methods were proposed and discussed during this period. Rashid and Al-Radhy ( 2014 ) discussed the issues of traditional teaching, learning, assessing methods of database courses at different universities in Kurdistan and the main focus of their study being reformation issues, such as absence of teaching determination and contradiction between content and theory. Similarly, Wang and Chen ( 2014 ) summarized the main problems in teaching the traditional database theory and its application. Curriculum assessment mode was the main focus of their study. Eaglestone and Nunes ( 2004 ) shared their experiences of delivering a databases design course at Sheffield University. Their focus of study included was to teach the database design module to a diverse group of students from different backgrounds. Rashid ( 2015 ) discussed some important features of database courses, whereby reforming the conventional teaching, learning, and assessing strategies of database courses at universities were the main focus of this study. Kui et al. ( 2018 ) reformed the teaching mode of database courses based on flipped classroom. Initiative learning of database courses was their main focus in this study. Similarly, Zhang et al. ( 2018 ) discussed several effective classroom teaching measures. The main focus of their study was teaching content, teaching methods, teaching evaluation and assessment methods. Cai and Gao ( 2019 ) also carried out the teaching reforms in the database course of liberal arts. Diversified teaching modes, such as flipping classroom, case oriented teaching and task oriented were the focus of their study. Teaching Kawash et al. ( 2020 ) proposed a learning approach called Graded Group Activities (GGAs). Their main focus of the study was reforming learning and assessment method.

Database course covers several topics that range from data modeling to data implementation and examination. Over the years, various authors have given their suggestions to update these topics in database curriculum to meet the requirements of modern technologies. On the other hand, authors have also proposed a new curriculum for the students of different academic backgrounds and different areas. These reformations in curriculum helped the students in their preparation, practically and theoretically, and enabled them to compete in the competitive market after graduation.

During 2003 and 2006 authors have proposed various suggestions to update and develop computer science curriculum across different universities. Robbert and Ricardo ( 2003 ) evaluated three reviews from 1999 to 2002 that were given to the groups of educators. The focus of their study was to highlight the trends that occurred in database curriculum. Also, Calero et al. ( 2003 ) proposed a first draft for this Database Body of Knowledge (DBBOK). Database (DB), Database Design (DBD), Database Administration (DBAd), Database Application (DBAp) and Advance Databases (ADVDB) were the main focus of their study. Furthermore, Conklin and Heinrichs (Conklin & Heinrichs, 2005 ) compared the content included in 13 database textbooks and the main focus of their study was IS 2002, CC2001, and CC2004 model curricula.

The years from 2007 and 2011, authors managed to developed various database curricula, like Luo et al. ( 2008 ) developed curricula in Zhejiang University City College. The aim of their study to nurture students to be qualified computer scientists. Likewise, Dietrich et al. ( 2008 ) proposed the techniques to assess the development of an advanced database course. The purpose behind the addition of an advanced database course at undergraduate level was to prepare the students to respond to industrial requirements. Also, Marshall ( 2011 ) developed a new database curriculum for Computer Science degree program in the South African context.

During 2012 and 2021 various authors suggested updates for the database curriculum such as Bhogal et al. ( 2012 ) who suggested updating and modernizing the database curriculum. Data management and data analytics were the focus of their study. Similarly, Picciano ( 2012 ) examined the curriculum in the higher level of American education. The focus of their study was big data and analytics. Also, Zhanquan et al. ( 2016 ) proposed the design for the course content and teaching methods in the classroom. Massive Open Online Courses (MOOCs) were the focus of their study. Likewise, Mingyu et al. ( 2017 ) suggested updating the database curriculum while keeping new technology concerning the database in perspective. The focus of their study was big data.

The above discussion clearly shows that the SQL is most discussed topic in the literature where more than 25% of the studies have discussed it in the previous decade as shown in Fig.  7 . It is pertinent to mention that other SQL databases such as Oracle, MS access are discussed under the SQL banner (Chen et al., 2012 ; Hou & Chen, 2010 ; Wang & Chen, 2014 ). It is mainly because of its ability to handle data in a relational database management system and direct implementation of database theoretical concepts. Also, other database topics such as transaction management, application programming etc. are also the main highlights of the topics discussed in the literature.

Evolution of Database topics discussed in literature

Research synthesis, advice for instructors, and way forward

This section presents the synthesized information extracted after reading and analyzing the research articles considered in this study. To this end, it firstly contextualizes the tools and methods to help the instructors find suitable tools and methods for their settings. Similarly, developments in curriculum design have also been discussed. Subsequently, general advice for instructors have been discussed. Lastly, promising future research directions for developing new tools, methods, and for revising the curriculum have also been discussed in this section.

Methods, tools, and curriculum

Methods and tools.

Web-based tools proposed by Cvetanovic et al. ( 2010 ) and Wang et al. ( 2010 ) have been quite useful, as they are growing increasingly pertinent as online mode of education is prevalent all around the globe during COVID-19. On the other hand, interactive tools and smart class room methodology has also been used successfully to develop the interest of students in database class. (Brusilovsky et al., 2010 ; Connolly et al., 2005 ; Pahl et al., 2004 ; Canedo et al., 2021 ; Ko et al., 2021 ).

One of the most promising combination of methodology and tool has been proposed by Cvetanovic et al. ( 2010 ), whereby they developed a tool named ADVICE that helps students learn and implement database concepts while using project centric methodology, while a game based collaborative learning environment was proposed by Connolly et al. ( 2005 ) that involves a methodology comprising of modeling, articulation, feedback, and exploration. As a whole, project centric teaching (Connolly & Begg, 2006 ; Domínguez & Jaime, 2010 ) and teaching database design and problem solving skills Wang and Chen ( 2014 ), are two successful approaches for DSE. Whereas, other studies (Urban & Dietrich, 1997 ) proposed teaching methods that are more inclined towards practicing database concepts. While a topic specific approach has been proposed by Abbasi et al. ( 2016 ), Taipalus et al. ( 2018 ) and Silva et al. ( 2016 ) to teach and learn SQL. On the other hand, Cai and Gao ( 2019 ) developed a teaching method for students who do not have a computer science background. Lastly, some useful ways for defining assessments for DSE have been proposed by Kawash et al. ( 2020 ) and Zhang et al. ( 2018 ).

Curriculum of database adopted by various institutes around the world does not address how to teach the database course to the students who do not have a strong computer science background. Such as Marshall ( 2012 ), Luo et al. ( 2008 ) and Zhanquan et al. ( 2016 ) have proposed the updates in current database curriculum for the students who are not from computer science background. While Abid et al. ( 2015 ) proposed a combined course content and various methodologies that can be used for teaching database systems course. On the other hand, current database curriculum does not include the topics related to latest technologies in database domain. This factor was discussed by many other studies as well (Bhogal et al., 2012 ; Mehmood et al., 2020 ; Picciano, 2012 ).

Guidelines for instructors

The major conclusion of this study are the suggestions based on the impact and importance for instructors who are teaching DSE. Furthermore, an overview of productivity of every method can be provided by the empirical studies. These instructions are for instructors which are the focal audience of this study. These suggestions are subjective opinions after literature analysis in form of guidelines according to the authors and their meaning and purpose were maintained. According to the literature reviewed, various issues have been found in this section. Some other issues were also found, but those were not relevant to DSE. Following are some suggestions that provide interesting information:

Project centric and applied approach

• To inculcate database development skills for the students, basic elements of database development need to be incorporated into teaching and learning at all levels including undergraduate studies (Bakar et al., 2011 ). To fulfill this objective, instructors should also improve the data quality in DSE by assigning the projects and assignments to the students where they can assess, measure and improve the data quality using already deployed databases. They should demonstrate that the quality of data is determined not only by the effective design of a database, but also through the perception of the end user (Mathieu & Khalil, 1997 )
• The gap between the database course theory and industrial practice is big. Fresh graduate students find it difficult to cope up with the industrial pressure because of the contrast between what they have been taught in institutes and its application in industry (Allsopp et al., 2006 ). Involve top performers from classes in industrial projects so that they are able to acquiring sufficient knowledge and practice, especially for post graduate courses. There must be some other activities in which industry practitioners come and present the real projects and also share their industrial experiences with the students. The gap between theoretical and the practical sides of database has been identified by Myers and Skinner ( 1997 ). In order to build practical DS concepts, instructors should provide the students an accurate view of reality and proper tools.

Importance of software development standards and impact of DB in software success

• They should have the strategies, ability and skills that can align the DSE course with the contemporary Global Software Development (GSD) (Akbar & Safdar, 2015 ; Damian et al., 2006 ).
• Enable the students to explain the approaches to problem solving, development tools and methodologies. Also, the DS courses are usually taught in normal lecture format. The result of this method is that students cannot see the influence on the success or failure of projects because they do not realize the importance of DS activities.

Pedagogy and the use of education technology

• Some studies have shown that teaching through play and practical activities helps to improve the knowledge and learning outcome of students (Dicheva et al., 2015 ).
• Interactive classrooms can help the instructors to deliver their lecture in a more effective way by using virtual white board, digital textbooks, and data over network(Abut & Ozturk, 1997 ). We suggest that in order to follow the new concept of smart classroom, instructors should use the experience of Yau and Karim ( 2003 ) which benefits in cooperative learning among students and can also be adopted in DSE.
• The instructors also need to update themselves with full spectrum of technology in education, in general, and for DSE, in particular. This is becoming more imperative as during COVID the world is relying strongly on the use of technology, particularly in education sector.

Periodic Curriculum Revision

• There is also a need to revisit the existing series of courses periodically, so that they are able to offer the following benefits: (a) include the modern day database system concepts; (b) can be offered as a specialization track; (c) a specialized undergraduate degree program may also be designed.

DSE: Way forward

This research combines a significant work done on DSE at one place, thus providing a point to find better ways forward in order to improvise different possible dimensions for improving the teaching process of a database system course in future. This section discusses technology, methods, and modifications in curriculum would most impact the delivery of lectures in coming years.

Several tools have already been developed for effective teaching and learning in database systems. However, there is a great room for developing new tools. Recent rise of the notion of “serious games” is marking its success in several domains. Majority of the research work discussed in this review revolves around web-based tools. The success of serious games invites researchers to explore this new paradigm of developing useful tools for learning and practice database systems concepts.

Likewise, due to COVID-19 the world is setting up new norms, which are expected to affect the methods of teaching as well. This invites the researchers to design, develop, and test flexible tools for online teaching in a more interactive manner. At the same time, it is also imperative to devise new techniques for assessments, especially conducting online exams at massive scale. Moreover, the researchers can implement the idea of instructional design in web-based teaching in which an online classroom can be designed around the learners’ unique backgrounds and effectively delivering the concepts that are considered to be highly important by the instructors.

The teaching, learning and assessment methods discussed in this study can help the instructors to improve their methods in order to teach the database system course in a better way. It is noticed that only 16% of authors have the assessment methods as their focus of study, which clearly highlights that there is still plenty of work needed to be done in this particular domain. Assessment techniques in the database course will help the learners to learn from their mistakes. Also, instructors must realize that there is a massive gap between database theory and practice which can only be reduced with maximum practice and real world database projects.

Similarly, the technology is continuously influencing the development and expansion of modern education, whereas the instructors’ abilities to teach using online platforms are critical to the quality of online education.

In the same way, the ideas like flipped classroom in which students have to prepare the lesson prior to the class can be implemented on web-based teaching. This ensures that the class time can be used for further discussion of the lesson, share ideas and allow students to interact in a dynamic learning environment.

The increasing impact of big data systems, and data science and its anticipated impact on the job market invites the researchers to revisit the fundamental course of database systems as well. There is a need to extend the boundaries of existing contents by including the concepts related to distributed big data systems data storage, processing, and transaction management, with possible glimpse of modern tools and technologies.

As a whole, an interesting and long term extension is to establish a generic and comprehensive framework that engages all the stakeholders with the support of technology to make the teaching, learning, practicing, and assessing easier and more effective.

This SLR presents review on the research work published in the area of database system education, with particular focus on teaching the first course in database systems. The study was carried out by systematically selecting research papers published between 1995 and 2021. Based on the study, a high level categorization presents a taxonomy of the published under the heads of Tools, Methods, and Curriculum. All the selected articles were evaluated on the basis of a quality criteria. Several methods have been developed to effectively teach the database course. These methods focus on improving learning experience, improve student satisfaction, improve students’ course performance, or support the instructors. Similarly, many tools have been developed, whereby some tools are topic based, while others are general purpose tools that apply for whole course. Similarly, the curriculum development activities have also been discussed, where some guidelines provided by ACM/IEEE along with certain standards have been discussed. Apart from this, the evolution in these three areas has also been presented which shows that the researchers have been presenting many different teaching methods throughout the selected period; however, there is a decrease in research articles that address the curriculum and tools in the past five years. Besides, some guidelines for the instructors have also been shared. Also, this SLR proposes a way forward in DSE by emphasizing on the tools: that need to be developed to facilitate instructors and students especially post Covid-19 era, methods: to be adopted by the instructors to close the gap between the theory and practical, Database curricula update after the introduction of emerging technologies such as big data and data science. We also urge that the recognized publication venues for database research including VLDB, ICDM, EDBT should also consider publishing articles related to DSE. The study also highlights the importance of reviving the curricula, tools, and methodologies to cater for recent advancements in the field of database systems.

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67 Data Management Essay Topics & Database Research Topics

🏆 best database research topics, ✍️ data management essay topics for college, 🎓 most interesting database topics for research paper, 💡 simple data management systems essay topics.

• Database Management Systems’ Major Capabilities
• Data Assets Management of LuLu Hypermarkets System
• Object-Oriented and Database Management Systems Tradeoffs
• Relational Database Management Systems in Business
• Big Data Opportunities in Green Supply Chain Management
• Deli Depot Case Study: Data Analysis Management Reporting
• Data Storage Management Solutions: Losses of Personal Data
• Technology-Assisted Reviews of Data in a Document Management System The TAR that is used in DMS falls into two major categories. These are automatic TAR and semi-automatic TAR, where the last implies the intervention of a human reviewer.
• Why Open-Source Software Will (Or Will Not) Soon Dominate the Field of Database Management Tools The study aims at establishing whether open-source software will dominate the database field because there has been a changing trend in the business market.
• Childhood Obesity: Data Management The use of electronic health records (EHR) is regarded as one of the effective ways to treat obesity in the population.
• Health Data Management: Sharing and Saving Patient Data One of the ways to facilitate achieving the idealized environment of data sharing is developing the methods of accessing health-related information.
• Data Management and Financial Strategies By adopting comprehensive supply chain management, businesses can maximize the three main streams in the supply chain— information flow, product flow, and money flow.
• Policy on People Data Management Law No. (13) of 2016 is a data protection legislation that applies to all public institutions and private organizations across Qatar.
• The Choice of a Medical Data Management System The choice of a medical data management system is critically important for any organization providing healthcare services.
• Data Analytics and Its Application to Management The role of the collection of data and its subsequent analysis in the industry is as big as ever. Specifically, it pertains to the managerial field.
• Modern Data Management and Organization Strategies Today, with a shrinking focus on data and analytics, a proper data management strategy is imperative to meeting business goals.
• Data Collection and Management Techniques for a Qualitative Research Plan To conduct complete qualitative research and present a cohesive qualitative research plan, it is necessary to match the structure and topic of the study.
• Database Management and Machine Learning Machine learning is used in science, business, industry, healthcare, education, etc. The possibilities of using machine learning technologies are constantly expanding.
• Data Management in a Medium-Sized Business This paper will use a medium-sized business data management offering highly specialized, high-quality business development education services as an example.
• Data Collection and Management Techniques of a Qualitative Research Plan This research paper recommends interview method in the collection of data and the application of NVivo statistical software in the management of data.
• Big Data Management Research This paper will present a literature review of three articles that examine text mining methods for quantitative analysis.
• Big Data Fraud Management The growth of eCommerce systems has led to an increase in online transactions using credit cards and other methods of payment services.
• Information Technology-Based Data Management in Retail The following paper discusses the specificities of data management and identifies the most apparent ethical considerations using retail as an example.
• Data Management, Networking and Enterprise Software Enterprise software is often created “in-house” and thus has a far higher cost as compared to simply buying the software solution from another company.
• EHR Database Management: Diabetes Prevention The data needed to prevent diabetes is usually collected throughout regular screenings conducted whenever a patient refers to a hospital, as well as by using various lab tests.
• Big Data Usage in Supply Chain Management This paper gives a summary of the research that was conducted to understand the unique issues surrounding the use of big data in the supply chain.
• Electronic Health Record Database and Data Management Progress in modern medicine has resulted in the amount of information related to the health of patients to grow exponentially.
• Adopting Electronic Data Management in the Health Care Industry
• Distributed Operating System and Infrastructure for Scientific Data Management
• Advanced Drill Data Management Solutions Market: Growth and Forecast
• The Changing Role of Data Management in Clinical Trials
• Business Rules and Their Relationship to Effective Data Management
• Class Enterprise Data Management and Administration
• Developing Highly Scalable and Autonomic Data Management
• Cloud Computing: Installation and Maintenance of Energy Efficient Data Management
• Exploring, Mapping, and Data Management Integration of Habitable Environments in Astrobiology
• Data Management: Data Warehousing and Data Mining
• Efficient Algorithmic Techniques for Several Multidimensional Geometric Data Management and Analysis Problems
• Data Management for Photovoltaic Power Plants Operation and Maintenance
• Elderly Patients and Falls: Adverse Trends and Data Management
• Data Management for Pre- and Post-Release Workforce Services
• Epidemiological Data Management During an Outbreak of Ebola Virus Disease
• Dealing With Identifier Variables in Data Management and Analysis
• How Data Mining, Data Warehousing, and On-Line Transactional Databases Are Helping Solve the Data Management Predicament
• Improving the New Data Management Technologies and Leverage
• Integrated Process and Data Management for Healthcare Applications
• Making Data Management Manageable: A Risk Assessment Activity for Managing Research Data
• The Use of Temporal Database in the Data Management System
• Multi-Cloud Data Management Using Shamir’s Secret Sharing and Quantum Byzantine Agreement Schemes
• Data Management Is More Than Just Managing Data
• Is Effective Data Management a Key Driver of Business Success?
• National Data Centre and Financial Statistics Office: A Conceptual Design for Public Data Management
• Big Data Management and Relevance of Big Data to E-Business
• Redefining the Data Management Strategy: A Way to Leverage the Huge Chunk of Data
• Structured Data Management Software Market in Taiwan
• Towards Effective GML Data Management: Framework and Prototype
• Data Management in Cloud Environments
• Digital Communication: Enterprise Data Management
• The Impact of Big Data on Data Management Functions
• Analysis of Data Management Strategies at Tesco
• The Best Data Management Tools Overview
• What Is Data Management and Why Is It Important
• Data Management and Use: Governance in the 21st Century
• What Is Data Management and How Do Businesses Use It?
• The Difference Between Data Management and Data Governance
• Types of Data Management Systems for Data-First Marketing Strategies and Success
• Reasons Why Data Management Leads to Business Success

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These essay examples and topics on Data Management were carefully selected by the StudyCorgi editorial team. They meet our highest standards in terms of grammar, punctuation, style, and fact accuracy. Please ensure you properly reference the materials if you’re using them to write your assignment.

This essay topic collection was updated on December 27, 2023 .

General Library Research Tutorial: Module 4: Searching a Database

• Module 1: Library Orientation
• Module 2: Developing a Topic
• Module 3: Understanding Source Types
• Module 4: Searching a Database
• Module 5: Evaluating Sources
• Module 6: Citing Sources

Learning Objectives

• Define the term research database.
• Differentiate between a subject and keyword search.
• Build a search using Boolean operators (AND, OR, NOT).
• Understand how to use truncation, nesting, phrase searching, and field-specific searching.

What is a Research Database?

A database is a searchable collection of information. Most  research databases are searchable collections of journal, magazine, and newspaper articles. Each database contains thousands of articles published in many different journals, allowing you find relevant articles faster than you would by searching individual journals.

Some databases provide the full text of articles. Others provide abstracts , or summaries, only.

Searching a Library database is different from searching the Internet.

Selecting a Database

Selecting the best research databases for your topic is an important step. You need to locate databases that cover your topic within the date range you need.

Find all of our databases on the Academic Databases page (from the Library website, click "Databases" in the menu bar). Use the "Subjects" dropdown menu to select your discipline. Skim through the list of databases to learn:

• Subjects covered
• Types of publications covered (e.g., journal articles, books, etc.)
• Dates covered

Keyword Searching

Keyword searches are similar to Google searches in that the database looks for your search terms wherever they may be on a page. Keyword searches search all available fields (e.g., Title, Author, Abstract, etc.) for the keyword.

In the example record below, you can see the keywords "video games" and "aggressive behavior" in bold in every field where they appear, including the Title, Subject Terms, and Abstract fields.

Subject Searching

Unlike keyword searches, subject searches only return results that include your search term in the subject headings field.

Many databases use a controlled vocabulary , which is a list of standardized subject headings used to index content. You can usually find the database's controlled vocabulary in a section called subject terms or the thesaurus . Use this tool to determine which word or phrase is the one used by the database for a specific concept. For example, since "adolescents" and "teenagers" mean roughly the same thing, a database may choose to index all articles on this topic under "teenagers." That way, a subject search for "teenagers" will also return articles about "adolescents."

In the database Academic Search Complete, we clicked "Subject Terms" in the blue menu bar. We then browsed for the term "adolescents." The search revealed that the preferred term in this database is "TEENAGERS."

Keyword vs. Subject Searching

Databases have different interfaces and use different subject terms, but most provide both keyword and subject searching. Let's take a closer look at the differences between these two search options.

Watch the video below to learn more.

Source: Wayne State University Libraries Instruction. “ Keyword vs. Subject Searching .” Online video clip. YouTube. YouTube, 9 January 2014. Web. 12 May 2017.

Phrase Searching

Place quotation marks around a phrase to search for that exact phrase. Most databases support phrase searching .

Example: A search for "United Nations" (with the quotation marks) will return only results where the two words appear together as a phrase.

For a quick demo, watch the video below.

Source: "Tips and Tricks: Phrase Searching" by North Carolina State University Libraries, licensed under a CC BY-NC-SA 3.0 US License .

Boolean Operators

When you want to combine search terms, you will need to use the Boolean operators , or connectors. This is best done using the advanced search mode. There are three main Boolean operators: AND, OR, and NOT.

Use AND to retrieve articles that mention  both terms  somewhere in the article. The use of AND generally will retrieve fewer but more focused results .

Example: Childhood obesity AND exercise

Use OR  between two terms to retrieve articles that mention  either term . The use of OR generally will retrieve a  larger set of results . The OR operator is useful when searching with terms that are synonyms or convey the same concept.

Example: Cloning OR genetics OR reproduction

Use NOT to exclude terms . The use of NOT allows you to remove search results containing a specific term. The use of NOT generally will retrieve fewer but more relevant results .

Example: Eating disorders NOT anorexia

Effective use of Boolean operators is essential to sophisticated research. Watch the video below to learn more about Boolean searching.

Source: fuliboutreach. “ Boolean Operators .” Online video clip. YouTube. YouTube, 30 September 2012. Web. 4 May 2017.

Field Specific Searching

A good technique for focusing a database search is to limit your search to a specific field. Do a field-specific search when you are looking for:

• articles in a particular journal
• items published in a particular year or years
• particular keywords in the title
• items written in English only

Example: Search for "Eating Disorders" as a keyword; search for "Gupta" in the Author field; search "Secondary Eating Disorder" in the Title field.

Truncation is a search technique that allows you to search for all variants of a root word at the same time. Enter the root word followed by the truncation symbol. Many databases use the asterisk (*)  for truncation. Others use the question mark (?). Check the Help page for the database you're using to determine which symbol to use for truncation.

Example: The search term plagiar* will return results that include terms:

• plagiarizing
• plagiarized
• plagiarizer
• plagiarizers

Broadening Your Search

Keep in mind that if you're looking for an all-in-one source that addresses your topic perfectly, you might need to change your approach. Watch this short video to learn what to do when you can't find enough resources on your topic.

Source: “One Perfect Source?” by North Carolina State University Libraries, licensed under a CC BY-NC-SA 3.0 US License .

Module 4 Quiz

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• Next: Module 5: Evaluating Sources >>
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99+ Interesting Data Science Research Topics For Students In 2024

In today’s information-driven world, data science research stands as a pivotal domain shaping our understanding and application of vast data sets. It amalgamates statistics, computer science, and domain knowledge to extract valuable insights from data. Understanding ‘What Is Data Science?’ is fundamental—a field exploring patterns, trends, and solutions embedded within data.

However, the significance of data science research papers in a student’s life cannot be overstated. They foster critical thinking, analytical skills, and a deeper comprehension of the subject matter. To aid students in navigating this realm effectively, this blog dives into essential elements integral to a data science research paper, while also offering a goldmine of 99+ engaging and timely data science research topics for 2024.

Unraveling tips for crafting an impactful research paper and insights on choosing the right topic, this blog is a compass for students exploring data science research topics. Stay tuned to unearth more about ‘data science research topics’ and refine your academic journey.

What Is Data Science?

Table of Contents

Data Science is like a detective for information! It’s all about uncovering secrets and finding valuable stuff in heaps of data. Imagine you have a giant puzzle with tons of pieces scattered around. Data Science helps in sorting these pieces and figuring out the picture they create. It uses tools and skills from math, computer science, and knowledge about different fields to solve real-world problems.

In simpler terms, Data Science is like a chef in a kitchen, blending ingredients to create a perfect dish. Instead of food, it combines data—numbers, words, pictures—to cook up solutions. It helps in understanding patterns, making predictions, and answering tricky questions by exploring data from various sources. In essence, Data Science is the magic that turns data chaos into meaningful insights that can guide decisions and make life better.

Importance Of Data Science Research Paper In Student’s Life

Data Science research papers are like treasure maps for students! They’re super important because they teach students how to explore and understand the world of data. Writing these papers helps students develop problem-solving skills, think critically, and become better at analyzing information. It’s like a fun adventure where they learn how to dig into data and uncover valuable insights that can solve real-world problems.

• Enhances critical thinking: Research papers challenge students to analyze and interpret data critically, honing their thinking skills.
• Fosters analytical abilities: Students learn to sift through vast amounts of data, extracting meaningful patterns and information.
• Encourages exploration: Engaging in research encourages students to explore diverse data sources, broadening their knowledge horizon.
• Develops communication skills: Writing research papers hones students’ ability to articulate complex findings and ideas clearly.
• Prepares for real-world challenges: Through research, students learn to apply theoretical knowledge to practical problems, preparing them for future endeavors.

Elements That Must Be Present In Data Science Research Paper

Here are some elements that must be present in data science research paper:

1. Clear Objective

A data science research paper should start with a clear goal, stating what the study aims to investigate or achieve. This objective guides the entire paper, helping readers understand the purpose and direction of the research.

2. Detailed Methodology

Explaining how the research was conducted is crucial. The paper should outline the tools, techniques, and steps used to collect, analyze, and interpret data. This section allows others to replicate the study and validate its findings.

3. Accurate Data Presentation

Presenting data in an organized and understandable manner is key. Graphs, charts, and tables should be used to illustrate findings clearly, aiding readers’ comprehension of the results.

4. Thorough Analysis and Interpretation

Simply presenting data isn’t enough; the paper should delve into a deep analysis, explaining the meaning behind the numbers. Interpretation helps draw conclusions and insights from the data.

5. Conclusive Findings and Recommendations

A strong conclusion summarizes the key findings of the research. It should also offer suggestions or recommendations based on the study’s outcomes, indicating potential avenues for future exploration.

Here are some interesting data science research topics for students in 2024:

Natural Language Processing (NLP)

• Multi-modal Contextual Understanding: Integrating text, images, and audio to enhance NLP models’ comprehension abilities.
• Cross-lingual Transfer Learning: Investigating methods to transfer knowledge from one language to another for improved translation and understanding.
• Emotion Detection in Text: Developing models to accurately detect and interpret emotions conveyed in textual content.
• Sarcasm Detection in Social Media: Building algorithms that can identify and understand sarcastic remarks in online conversations.
• Language Generation for Code: Generating code snippets and scripts from natural language descriptions using NLP techniques.
• Bias Mitigation in Language Models: Developing strategies to mitigate biases present in large language models and ensure fairness in generated content.
• Dialogue Systems for Personalized Assistance: Creating intelligent conversational agents that provide personalized assistance based on user preferences and history.
• Summarization of Legal Documents: Developing NLP models capable of summarizing lengthy legal documents for quick understanding and analysis.
• Understanding Contextual Nuances in Sentiment Analysis: Enhancing sentiment analysis models to better comprehend contextual nuances and sarcasm in text.
• Hate Speech Detection and Moderation: Building systems to detect and moderate hate speech and offensive language in online content.

Computer Vision

• Weakly Supervised Object Detection: Exploring methods to train object detection models with limited annotated data.
• Video Action Recognition in Uncontrolled Environments: Developing models that can recognize human actions in videos captured in uncontrolled settings.
• Image Generation and Translation: Investigating techniques to generate realistic images from textual descriptions and translate images across different domains.
• Scene Understanding in Autonomous Systems: Enhancing computer vision algorithms for better scene understanding in autonomous vehicles and robotics.
• Fine-grained Visual Classification: Improving models to classify objects at a more granular level, distinguishing subtle differences within similar categories.
• Visual Question Answering (VQA): Creating systems capable of answering questions based on visual input, requiring reasoning and comprehension abilities.
• Medical Image Analysis for Disease Diagnosis: Developing computer vision models for accurate and early diagnosis of diseases from medical images.
• Action Localization in Videos: Building models to precisely localize and recognize specific actions within video sequences.
• Image Captioning with Contextual Understanding: Generating captions for images considering the context and relationships between objects.
• Human Pose Estimation in Real-time: Improving algorithms for real-time estimation of human poses in videos for applications like motion analysis and gaming.

Machine Learning Algorithms

• Self-supervised Learning Techniques: Exploring novel methods for training machine learning models without explicit supervision.
• Continual Learning in Dynamic Environments: Investigating algorithms that can continuously learn and adapt to changing data distributions and tasks.
• Explainable AI for Model Interpretability: Developing techniques to explain the decisions and predictions made by complex machine learning models.
• Transfer Learning for Small Datasets: Techniques to effectively transfer knowledge from large datasets to small or domain-specific datasets.
• Adaptive Learning Rate Optimization: Enhancing optimization algorithms to dynamically adjust learning rates based on data characteristics.
• Robustness to Adversarial Attacks: Building models resistant to adversarial attacks, ensuring stability and security in machine learning applications.
• Active Learning Strategies: Investigating methods to select and label the most informative data points for model training to minimize labeling efforts.
• Privacy-preserving Machine Learning: Developing algorithms that can train models on sensitive data while preserving individual privacy.
• Fairness-aware Machine Learning: Techniques to ensure fairness and mitigate biases in machine learning models across different demographics.
• Multi-task Learning for Jointly Learning Tasks: Exploring approaches to jointly train models on multiple related tasks to improve overall performance.

Deep Learning

• Graph Neural Networks for Representation Learning: Using deep learning techniques to learn representations from graph-structured data.
• Transformer Models for Image Processing: Adapting transformer architectures for image-related tasks, such as image classification and generation.
• Few-shot Learning Strategies: Investigating methods to enable deep learning models to learn from a few examples in new categories.
• Memory-Augmented Neural Networks: Enhancing neural networks with external memory for improved learning and reasoning capabilities.
• Neural Architecture Search (NAS): Automating the design of neural network architectures for specific tasks or constraints.
• Meta-learning for Fast Adaptation: Developing models capable of quickly adapting to new tasks or domains with minimal data.
• Deep Reinforcement Learning for Robotics: Utilizing deep RL techniques for training robots to perform complex tasks in real-world environments.
• Generative Adversarial Networks (GANs) for Data Augmentation: Using GANs to generate synthetic data for enhancing training datasets.
• Variational Autoencoders for Unsupervised Learning: Exploring VAEs for learning latent representations of data without explicit supervision.
• Lifelong Learning in Deep Networks: Strategies to enable deep networks to continually learn from new data while retaining past knowledge.

Big Data Analytics

• Streaming Data Analysis for Real-time Insights: Techniques to analyze and derive insights from continuous streams of data in real-time.
• Scalable Algorithms for Massive Graphs: Developing algorithms that can efficiently process and analyze large-scale graph-structured data.
• Anomaly Detection in High-dimensional Data: Detecting anomalies and outliers in high-dimensional datasets using advanced statistical methods and machine learning.
• Personalization and Recommendation Systems: Enhancing recommendation algorithms for providing personalized and relevant suggestions to users.
• Data Quality Assessment and Improvement: Methods to assess, clean, and enhance the quality of big data to improve analysis and decision-making.
• Time-to-Event Prediction in Time-series Data: Predicting future events or occurrences based on historical time-series data.
• Geospatial Data Analysis and Visualization: Analyzing and visualizing large-scale geospatial data for various applications such as urban planning, disaster management, etc.
• Privacy-preserving Big Data Analytics: Ensuring data privacy while performing analytics on large-scale datasets in distributed environments.
• Graph-based Deep Learning for Network Analysis: Leveraging deep learning techniques for network analysis and community detection in large-scale networks.
• Dynamic Data Compression Techniques: Developing methods to compress and store large volumes of data efficiently without losing critical information.

Healthcare Analytics

• Predictive Modeling for Patient Outcomes: Using machine learning to predict patient outcomes and personalize treatments based on individual health data.
• Clinical Natural Language Processing for Electronic Health Records (EHR): Extracting valuable information from unstructured EHR data to improve healthcare delivery.
• Wearable Devices and Health Monitoring: Analyzing data from wearable devices to monitor and predict health conditions in real-time.
• Drug Discovery and Development using AI: Utilizing machine learning and AI for efficient drug discovery and development processes.
• Predictive Maintenance in Healthcare Equipment: Developing models to predict and prevent equipment failures in healthcare settings.
• Disease Clustering and Stratification: Grouping diseases based on similarities in symptoms, genetic markers, and response to treatments.
• Telemedicine Analytics: Analyzing data from telemedicine platforms to improve remote healthcare delivery and patient outcomes.
• AI-driven Radiomics for Medical Imaging: Using AI to extract quantitative features from medical images for improved diagnosis and treatment planning.
• Healthcare Resource Optimization: Optimizing resource allocation in healthcare facilities using predictive analytics and operational research techniques.
• Patient Journey Analysis and Personalized Care Pathways: Analyzing patient trajectories to create personalized care pathways and improve healthcare outcomes.

Time Series Analysis

• Forecasting Volatility in Financial Markets: Predicting and modeling volatility in stock prices and financial markets using time series analysis.
• Dynamic Time Warping for Similarity Analysis: Utilizing DTW to measure similarities between time series data, especially in scenarios with temporal distortions.
• Seasonal Pattern Detection and Analysis: Identifying and modeling seasonal patterns in time series data for better forecasting.
• Time Series Anomaly Detection in Industrial IoT: Detecting anomalies in industrial sensor data streams to prevent equipment failures and improve maintenance.
• Multivariate Time Series Forecasting: Developing models to forecast multiple related time series simultaneously, considering interdependencies.
• Non-linear Time Series Analysis Techniques: Exploring non-linear models and methods for analyzing complex time series data.
• Time Series Data Compression for Efficient Storage: Techniques to compress and store time series data efficiently without losing crucial information.
• Event Detection and Classification in Time Series: Identifying and categorizing specific events or patterns within time series data.
• Time Series Forecasting with Uncertainty Estimation: Incorporating uncertainty estimation into time series forecasting models for better decision-making.
• Dynamic Time Series Graphs for Network Analysis: Representing and analyzing dynamic relationships between entities over time using time series graphs.

Reinforcement Learning

• Multi-agent Reinforcement Learning for Collaboration: Developing strategies for multiple agents to collaborate and solve complex tasks together.
• Hierarchical Reinforcement Learning: Utilizing hierarchical structures in RL for solving tasks with varying levels of abstraction and complexity.
• Model-based Reinforcement Learning for Sample Efficiency: Incorporating learned models into RL for efficient exploration and planning.
• Robotic Manipulation with Reinforcement Learning: Training robots to perform dexterous manipulation tasks using RL algorithms.
• Safe Reinforcement Learning: Ensuring that RL agents operate safely and ethically in real-world environments, minimizing risks.
• Transfer Learning in Reinforcement Learning: Transferring knowledge from previously learned tasks to expedite learning in new but related tasks.
• Curriculum Learning Strategies in RL: Designing learning curricula to gradually expose RL agents to increasingly complex tasks.
• Continuous Control in Reinforcement Learning: Exploring techniques for continuous control tasks that require precise actions in a continuous action space.
• Reinforcement Learning for Adaptive Personalization: Utilizing RL to personalize experiences or recommendations for individuals in dynamic environments.
• Reinforcement Learning in Healthcare Decision-making: Using RL to optimize treatment strategies and decision-making in healthcare settings.

Data Mining

• Graph Mining for Social Network Analysis: Extracting valuable insights from social network data using graph mining techniques.
• Sequential Pattern Mining for Market Basket Analysis: Discovering sequential patterns in customer purchase behaviors for market basket analysis.
• Clustering Algorithms for High-dimensional Data: Developing clustering techniques suitable for high-dimensional datasets.
• Frequent Pattern Mining in Healthcare Datasets: Identifying frequent patterns in healthcare data for actionable insights and decision support.
• Outlier Detection and Fraud Analysis: Detecting anomalies and fraudulent activities in various domains using data mining approaches.
• Opinion Mining and Sentiment Analysis in Reviews: Analyzing opinions and sentiments expressed in product or service reviews to derive insights.
• Data Mining for Personalized Learning: Mining educational data to personalize learning experiences and adapt teaching methods.
• Association Rule Mining in Internet of Things (IoT) Data: Discovering meaningful associations and patterns in IoT-generated data streams.
• Multi-modal Data Fusion for Comprehensive Analysis: Integrating information from multiple data modalities for a holistic understanding and analysis.
• Data Mining for Energy Consumption Patterns: Analyzing energy usage data to identify patterns and optimize energy consumption in various sectors.

Ethical AI and Bias Mitigation

• Fairness Metrics and Evaluation in AI Systems: Developing metrics and evaluation frameworks to assess the fairness of AI models.
• Bias Detection and Mitigation in Facial Recognition: Addressing biases present in facial recognition systems to ensure equitable performance across demographics.
• Algorithmic Transparency and Explainability: Designing methods to make AI algorithms more transparent and understandable to stakeholders.
• Fair Representation Learning in Unbalanced Datasets: Learning fair representations from imbalanced data to reduce biases in downstream tasks.
• Fairness-aware Recommender Systems: Ensuring fairness and reducing biases in recommendation algorithms across diverse user groups.
• Ethical Considerations in AI for Criminal Justice: Investigating the ethical implications of AI-based decision-making in criminal justice systems.
• Debiasing Techniques in Natural Language Processing: Developing methods to mitigate biases in language models and text generation.
• Diversity and Fairness in Hiring Algorithms: Ensuring diversity and fairness in AI-based hiring systems to minimize biases in candidate selection.
• Ethical AI Governance and Policy: Examining the role of governance and policy frameworks in regulating the development and deployment of AI systems.
• AI Accountability and Responsibility: Addressing ethical dilemmas and defining responsibilities concerning AI system behaviors and decision-making processes.

Tips For Writing An Effective Data Science Research Paper

Here are some tips for writing an effective data science research paper:

Tip 1: Thorough Planning and Organization

Begin by planning your research paper carefully. Outline the sections and information you’ll include, ensuring a logical flow from introduction to conclusion. This organized approach makes writing easier and helps maintain coherence in your paper.

Tip 2: Clarity in Writing Style

Use clear and simple language to communicate your ideas. Avoid jargon or complex terms that might confuse readers. Write in a way that is easy to understand, ensuring your message is effectively conveyed.

Tip 3: Precise and Relevant Information

Include only information directly related to your research topic. Ensure the data, explanations, and examples you use are precise and contribute directly to supporting your arguments or findings.

Tip 4: Effective Data Visualization

Utilize graphs, charts, and tables to present your data visually. Visual aids make complex information easier to comprehend and can enhance the overall presentation of your research findings.

Tip 5: Review and Revise

Before submitting your paper, review it thoroughly. Check for any errors in grammar, spelling, or formatting. Revise sections if necessary to ensure clarity and coherence in your writing. Asking someone else to review it can also provide valuable feedback.

• Hospitality Management Research Topics

Things To Remember While Choosing The Data Science Research Topic

When selecting a data science research topic, consider your interests and its relevance to the field. Ensure the topic is neither too broad nor too narrow, striking a balance that allows for in-depth exploration while staying manageable.

• Relevance and Significance: Choose a topic that aligns with current trends or addresses a significant issue in the field of data science.
• Feasibility : Ensure the topic is researchable within the resources and time available. It should be practical and manageable for the scope of your study.
• Your Interest and Passion: Select a topic that genuinely interests you. Your enthusiasm will drive your motivation and engagement throughout the research process.
• Availability of Data: Check if there’s sufficient data available for analysis related to your chosen topic. Accessible and reliable data sources are vital for thorough research.
• Potential Contribution: Consider how your chosen topic can contribute to existing knowledge or fill a gap in the field. Aim for a topic that adds value and insights to the data science domain.

In wrapping up our exploration of data science research topics, we’ve uncovered a world of importance and guidance for students. From defining data science to understanding its impact on student life, identifying essential elements in research papers, offering a multitude of intriguing topics for 2024, to providing tips for crafting effective papers—the journey has been insightful. 

Remembering the significance of topic selection and the key components of a well-structured paper, this voyage emphasizes how data science opens doors to endless opportunities. It’s not just a subject; it’s the compass guiding tomorrow’s discoveries and innovations in our digital landscape.

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Research Guides

Information Literacy & Library Research: Topics and Background Research

• Table of Contents
• Information Literacy
• Research Process
• Topics and Background Research
• Writing a Research Question
• Source Types
• Keyword Basics
• Research: A Journey in Small Steps
• Keywords and Boolean Operators
• Using Databases
• How to Find Books and eBooks
• Popular vs Scholarly
• "Search the Library" through the EBSCO Discovery Service
• Applying the CRAAP Test to Sources
• Citing with MLA 9
• Information Synthesis
• How to Critically Read Academic Articles
• Information Has Value
• How to Avoid Plagiarism
• Module 6. Reflecting
• Academic Honesty and Plagiarism
• Copyright and Fair Use
• Creative Commons Licenses
• Information has Value
• Joining the Scholarly Conversation
• Library Classification Systems
• Google Scholar
• Subject Databases
• Find Journal by Title
• Advanced Search Strategies
• MLA Style Examples
• APA Style Examples

Your Research Journey Starts with a Good Topic

The reason you want to pick a topic that is interesting to you is so that researching becomes an investigative journey rather than a dredge through a swamp of information. But this doesn't make it easy. Sometimes just picking a topic can be the hardest part of your research, especially when you can pick any possible topic in existence.

Starting from topics that interest you is key. What have you been thinking about lately? What news have you been following? What do you enjoy doing? What is something you are curious about that you don't know anything about? No matter how weird the topic is, there is usually a way to make it into a topic that is researchable on an academic level.

But doing that is going to take a bit of work. So here are some things to consider before getting started:

• What type of presentation or paper is required?  Are you writing an argumentative essay, expressing your opinion, analyzing the facts you've gathered, gathering sources for a bibliography, or giving a speech?
• How long is the presentation? Are you writing a 5-6 page paper, a bibliography, or giving a 5-minute speech?
• How many and what kind of sources are required? Can these sources be popular books, articles from popular magazines or newspapers, and Web sites, or are scholarly sources required?
• What format is required for your writing assignment?  MLA, APA, or another?
• What is the due date?
• Try to avoid overused topic ideas.  Topics like abortion, gun control, teen pregnancy, assisted suicide, or athlete drug abuse are often chosen by students and tend to be overused. If you must use these topics, try to think of a unique perspective on the topic. 
• Choose a topic that interests you.  Personal interest makes research more enjoyable and if it is of interest to you, you'll probably do a better job of writing.
• For INFO 1010 , this means picking a topic that is interesting to you, but also works within the theme of your ENGL 2010 class, if you are taking these classes together. Think about whether or not the topic will work for a 7-10 page paper.

Brainstorming a Topic

Brainstorming is a good way to explore topic paths that you can take and is a vital part of the planning part of research. There are many ways to brainstorm, so choose the way that makes the most sense to your learning style. Be as visual or as textual as you want.

The main point of brainstorming is to explore connections between concepts within a topic. For example, if you really love dogs, you can look into different aspects of what you like about dogs. Even a topic as broad as dogs has a lot of different researchable sub-topics.

Start making a list of the things you like about dogs:

• Dog breeding
• Dog walking
• Dogs are happy
• Dogs make people healthier

It helps to look at topics in the news and other media. Usually the news will report on new and breakthrough studies that are related to your topic, so doing a quick search will give you ideas about where to begin when it comes to brainstorming directions your topic can go in.

Once you have a list, you can start exploring the connections. Some people like to create a mind map to connect their ideas and visualize a topic. This can help you see the connections and brainstorm even further as you start coming up with ideas and seeing how they relate to each other. (Some free mind mapping tools you might check out include: Miro , Canva , Figma , Invision , and Mindomo ).

Google Searches

As you learn more about your topic, you'll know what direction to take it in. Brainstorming alone won't provide you with all the possible sub-topics to choose from. Doing a quick search on Google or another search engine can help you see what people have written about the topic and provide you with additional sub-topic ideas. Background searching helps you see the big picture.

For example, If someone was interested in researching marijuana use because it's all over the news, they could do a quick search online and look at some of the top websites. This would give them some context for what's going on in the news, what is generally said or believed about it, and could give them ideas about what more they want to learn about.

You can add what you've learned from your Google search to your mind map to make even more connections. Maybe you want to know how one facet of your topic relates to another. How does dog walking relate to mental and physical health? Is there a connection between puppy farming and animal cruelty? The more you learn the basics of the topic, the more you will know what you want to know more about, in greater detail, enough for you to write you paper on.

INFO 1010 Note:

This is the process that you will follow in the INFO 1010 Module 2 assignment, where you start with your broad topic and use Google searches to get ideas about keywords and phrases to help narrow your topic.

Background Research

Wikipedia searching.

One resource you're probably familiar with is Wikipedia. You've probably been told that you can't use it in your papers. That's only true if you are trying to use it as a cited source. As an encyclopedia, it's too general to use as a cited source, but it is great for getting the background information you need.

Use Wikipedia (or any other encyclopedia ) to understand the basics of the topic. Skimming the introduction can give you a good summary of the broad topic. Usually there are hyperlinks to specific aspects of the topic that can provide you with ideas for how to broaden or narrow your focus. For example, if you were interested in Marijuana, just the first couple of paragraphs links to pages on psychoactive drugs, recreational drugs, and medicinal drugs. It also highlights the common ways it is used, as well as the effects, both short and long-term. Any of these could lead to a more focused version of your topic.

Looking at the table of contents can also help you see the different directions you could potentially take as you quickly go to the sections and read some basic details about that aspect of the topic.

Taking Notes while Searching

Just reading can be helpful, but make sure you take notes while reading the different articles and sections to help you keep track of subtopics and facts that you are more interested in. You should also m ake note of terms you see frequently while reading the sections that interest you. These are often more technical terms. For example, m arijuana is also called cannabis. "Recreational marijuana" refers to when it's used for fun, while "medicinal marijuana" or "medical cannabis" are terms used when it's used to treat patients. These are frequently the keywords that you will be using when you start searching in the library databases.

Additional Resources

Where to look for topic ideas in the SUU Library web page:

• SUU Reference sources can be found on the  SUU Library Website  and in the  Reference Collection  on the first floor of the library.

Below are a few general online reference sources, (encyclopedias, dictionaries, handbooks), that may help you find a topic that interests you:

• Oxford Reference Online   is an example of a reference source that may spark ideas for research topics and provide excellent background information. 
• CQ Researcher   is a database of reports on current and controversial topics. The reports provide background information, Pro/Con views, maps and graphs, and bibliographies with additional information sources. 
• Opposing Viewpoints  is another database with articles on social issues including pro/con  essays , topic overviews, primary source documents,  periodical  articles, and data.

These  databa ses  can all be found by selecting  Find a Database  which is just under the "Search the Library" search bar, then selecting  General Research  from the  All Subjects  drop down box on the top left-hand side of the page.

Here are some more subject specific encyclopedias:

Biography Reference Bank (H.W. Wilson)

CRC Handbook of Chemistry and Physics

Educator's Reference Desk

History Reference Center

Legal Information Reference Center

Small Business Reference Center

• << Previous: Module 2. Planning
• Next: Writing a Research Question >>
• Last Updated: Apr 10, 2024 2:20 PM
• URL: https://library.suu.edu/LibraryResearch

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What's happening at the library and items of interest to the UB community

Using Databases to Find a Research Paper Topic

It’s almost mid-semester and you still haven’t picked a topic for your research paper. No worries! The RLB Library has a few tips and tools that can help you find a topic.

Your topic can be on something you’d like to learn more about, or about an issue that is relevant today. It helps to choose a topic that is broad enough to allow research on several aspects of an issue, but not too broad that you find yourself going off on tangents. The topic should be interesting to you and perhaps meaningful in some way to today’s society. In addition, you should be able to support your ideas with research from appropriate sources.

A good way to look for topics is to  read lots of stuff  in the general subject areas that interest you. The RLB Library has tools for you to find reading material online. These tools include:

1. Research Starters

Go to the Library’s homepage library.ubalt.edu and type in some keywords in the gray box under the “library search” tab. For example, the keyword “inflation” will result in a “Research Starter” display at the top of the search results. Research Starters are a good way to get an overview of a topic. Clicking on “more” will take you to a detailed article on the topic.

2. Credo Reference

This database is another great place to read background information on many topics. You can access Credo Reference by going to the library homepage and clicking on “Databases” beneath the search box. When you get to the list of A-Z Databases, click on the letter C and scroll down for “Credo Reference.”

In Credo Reference, you can browse by subjects. On the right side there are useful “Research Quick Tips.” You can also use “mind maps” to explore related concepts.

3. Academic OneFile (Gale)

This database includes a Topic Finder. When you input a search term, a diagram appears with “tiles” that you can click on to narrow your search and pull up relevant articles. You can access Academic OneFile through the A-Z Databases list under “A.”

4. Opposing Viewpoints (Gale in Context)

If you are interested in contemporary issues, the database Opposing Viewpoints is a good starting point to read about current social issues, with articles exploring contrasting viewpoints. This database can also be accessed from the A-Z Databases list under “O.”

In summary, finding a topic for your research paper or project can be made easier by reading background material. The four resources mentioned above can help you find those background articles that point you to an interesting and compelling topic. But don’t procrastinate! You need to set aside time to read.   Ask a Librarian if you want more information on the research process or on how to pick a good topic.

**For a really good tutorial demonstrating how to find a topic, check out this video from NC State University Libraries.**

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iNetTutor.com

Online Programming Lessons, Tutorials and Capstone Project guide

40 List of DBMS Project Topics and Ideas

Introduction

A Capstone project is the last project of an IT degree program. It is made up of one or more research projects in which students create prototypes, services, and/or products. The projects are organized around an issue that needs to be handled in real-world scenarios. When IT departments want to test new ideas or concepts that will be adopted into their daily operations, they implement these capstone projects within their services.

In this article, our team has compiled a list of Database Management System Project Topics and Ideas. The capstone projects listed below will assist future researchers in deciding which capstone project idea to pursue. Future researchers may find the information in this page useful in coming up with unique capstone project ideas.

• Telemedicine Online Platform Database Design

  “Telemedicine Online Platform” is designed to allow doctors to deliver clinical support to patients remotely. Doctors can communicate with their patients in real-time for consultations, diagnoses, monitoring, and medical supply prescriptions. The project will be developed using the SDLC method by the researchers. The researchers will also compile a sample of hospital doctors and patients who will act as study participants. A panel of IT specialists will review, test, and assess the project.

• Virtual and Remote Guidance Counselling System Database Design

Counseling is a vital component of a person’s life since it aids in the improvement of interpersonal relationships. Humans must cease ignoring this issue because it is essential for the development of mental wellness. The capstone project “Virtual and Remote Guidance Counselling System,” which covers the gap in giving counseling in stressful situations, was built for this reason. It answers to the requirement to fill in the gaps in the traditional technique and make it more effective and immersive in this way.

• COVID-19 Facilities Management Information System Database Design

COVID – 19 has put people in fear due to its capability of transmission when exposed to the virus. The health sectors and the government provide isolation facilities for COVID-19 patients to mitigate the spread and transmission of the virus. However, proper communication for the availability of the facilities is inefficient resulting to surge of patients in just one facility and some are transferred multiple times due to unavailability. The COVID-19 respondents must have an advance tools to manage the COVID-19 facilities where respondents can easily look for available facilities to cater more patients.

• Document Tracking System Database Design

The capstone project, “Document Tracking System” is purposely designed for companies and organizations that allow them to electronically store and track documents. The system will track the in/out of the documents across different departments. The typical way of tracking documents is done using the manual approach. The staff will call or personally ask for updates about the documents which are time-consuming and inefficient.

• Face Recognition Application Database Design

Technology has grown so fast; it changes the way we do our daily tasks. Technology has made our daily lives easier. The capstone project, entitled “Face Recognition Attendance System” is designed to automate checking and recording of students’ attendance during school events using face recognition technology. The system will work by storing the student’s information along with their photographs in a server and the system will detect the faces of the students during school events and match it and verify to record the presence or absence of the student.

• Digital Wallet Solution Database Design

The capstone project, named “Digital Wallet Solution,” is intended to allow people to store money online and make payments online. The digital wallet transactions accept a variety of currencies and provide a variety of payment gateways via which the user can pay for products and services. The system allows users to conduct secure and convenient online financial transactions. It will speed up payment and other financial processes, reducing the amount of time and effort required to complete them.

• Virtual Online Tour Application Database Design

The usage of technology is an advantage in the business industry, especially during this challenging pandemic. It allows businesses to continue to operate beyond physicality. The capstone project entitled “Virtual Online Tour Application” is designed as a platform to streamline virtual tours for clients. Any business industry can use the system to accommodate and provide their clients with a virtual experience of their business. For example, the tourist industry and real estate agencies can use the system to provide a virtual tour to their clients about the tourist locations and designs of properties, respectively.

Invoice Management System Database Design

The researchers will create a system that will make it easier for companies to manage and keep track of their invoice information. The company’s sales records, payables, and total invoice records will all be electronically managed using this project. Technology is highly used for business operations and transactions automation. The capstone project, entitled “Invoice Management System” is designed to automate the management of the company’s invoice records. The said project will help companies to have an organized, accurate, and reliable record that will help them track their sales and finances.

• Vehicle Repair and Maintenance Management System Database Design

Information Technology has become an integral part of any kind of business in terms of automating business operations and transactions. The capstone project, entitled “Vehicle Repair and Maintenance Management System” is designed for vehicle repair and maintenance management automation. The said project will automate the vehicle garage’s operations and daily transactions. The system will automate operations such as managing vehicle repair and maintenance records, invoice records, customer records, transaction records, billing and payment records, and transaction records.

• Transcribe Medical Database Design

Information technology has made everything easier and simpler, including transcribing the medical diagnosis of patients. The capstone project, entitled “Medical Transcription Platform,” is designed to allow medical transcriptionists to transcribe audio of medical consultations and diagnose patients in a centralized manner. A medical transcriptionist is vital to keep accurate and credible medical records of patients and can be used by other doctors to know the patients’ medical history. The said project will serve as a platform where transcribed medical audios are stored for safekeeping and easy retrieval.

• Multi-branch Travel Agency and Booking System Database Design

The capstone project, entitled “Multi-Branch Travel Agency and Booking System,” is designed as a centralized platform wherein multiple travel agency branches are registered to ease and simplify inquiries and booking of travels and tour packages by clients. The said project will allow travel agencies to operate a business in an easy, fast manner considering the convenience and safety of their clients. The system will enable travel agencies and their clients to have a seamless online transaction.

• Pharmacy Stocks Management Database Design

The capstone project “Pharmacy Stocks Management System” allows pharmacies to manage and monitor their stocks of drugs electronically. The Pharmacy Stocks Management System will automate inventory to help ensure that the pharmacy has enough stock of medications and supplies to serve the needs of the patients.

• Loan Management with SMS Database Design

The capstone project entitled “ Loan Management System with SMS ” is an online platform that allows members to apply and request loan. In addition, they can also monitor their balance in their respective dashboard. Management of cooperative will review first the application for approval or disapproval of the request. Notification will be send through the SMS or short messaging service feature of the system.

• Service Call Management System Database Design

The capstone project, entitled ” Service Call Management System,” is designed to transform service calls to a centralized platform. The said project would allow clients to log in and lodge calls to the tech support if they encountered issues and difficulties with their purchased products. The tech support team will diagnose the issue and provide them with the necessary actions to perform via a call to solve the problem and achieve satisfaction.

• File Management with Approval Process Database Design

The File Management System provides a platform for submitting, approving, storing, and retrieving files. Specifically, the capstone project is for the file management of various business organizations. This is quite beneficial in the management and organization of the files of every department. Installation of the system on an intranet is possible, as is uploading the system to a live server, from which the platform can be viewed online and through the use of a browser.

• Beauty Parlor Management System Database Design

The capstone project entitled “Beauty Parlour Management System” is an example of transactional processing system that focuses on the records and process of a beauty parlour. This online application will help the management to keep and manage their transactions in an organize, fast and efficient manner.

• Exam Management System Database Design

Information technology plays a significant role in the teaching and learning process of teachers and students, respectively. IT offers a more efficient and convenient way for teachers and students to learn and assess learnings. The capstone project, “Exam Management System,” is designed to allow electronic management of all the information about the exam questions, courses and subjects, and teachers and students. The said project is an all-in-one platform for student exam management.

• Student and Faculty Clearance Database Design

The capstone project, entitled “Student and Faculty Clearance System,” is designed to automate students and faculty clearance processes. The approach is intended to make the clearance procedure easier while also guaranteeing that approvals are accurate and complete. The project works by giving every Department involved access to the application. The proposed scheme can eliminate the specified challenges, streamline the process, and verify the integrity and correctness of the data.

• Vehicle Parking Management System Database Design

The capstone project entitled “ Vehicle Parking Management System ” is an online platform that allows vehicle owners to request or reserve a slot for parking space. Management can accept and decline the request of reservation. In addition, payment option is also part of the system feature but is limited to on-site payment.

• Hospital Resources and Room Utilization Database Design

The capstone project, “Hospital Resources and Room Utilization Management System” is a system designed to streamline the process of managing hospital resources and room utilization. The said project is critical especially now that we are facing a pandemic, there is a need for efficient management of hospital resources and room management. The management efficiency will prevent a shortage in supplies and overcrowding of patients in the hospitals.

• Church Event Management System Database Design

The capstone project entitled “Church Event Management System” is designed to be used by church organizations in creating and managing different church events. The conventional method of managing church events is done manually where members of organizations will face difficulties due to physical barriers and time constraints.

• CrowdFunding Platform Database Design

Business financing is critical for new business ventures. In this study, the researchers concentrate on designing and developing a business financing platform that is effective for new startups. This capstone project, entitled “Crowdfunding Platform” is a website that allows entrepreneurs to campaign their new business venture to attract investors and crowdfund.

• Vehicle Franchising and Drivers Offense Software Database Design

The proposed software will be used to electronically process and manage vehicle and franchising and driver’s offenses. The proposed software will eliminate the manual method which involves a lot of paper works and consumes valuable amount of time. The proposed project will serve as a centralized platform was recording and paying for the offenses committed by the drivers will be processed. The system will quicken the process of completing transaction between the enforcers and the drivers. Vehicle franchising and managing driver offenses will be easy, fast and convenient using the system.

• Student Tracking Performance Database Design

The capstone project entitled “Student Academic Performance Tracking and Monitoring System” allows academic institutions to monitor and gather data about the academic performance of students where decisions are derived to further improve the students learning outcomes. Tracking and monitoring student’s performance serves a vital role in providing information that is used to assist students, teachers, administrators, and policymakers in making decisions that will further improve the academic performance of students.

• Webinar Course Management System Database Design

The capstone project, entitled “Webinar Course Management System,” is designed to automate managing webinar courses. The project aims to eliminate the current method, which is inefficient and inconvenient for parties involved in the webinar. A software development life cycle (SDLC) technique will be used by the researchers in order to build this project. They will gather a sample size of participating webinar members and facilitators to serve as respondents of the study.

• Online Birth Certificate Processing System with SMS Notification Database Design

The capstone project, “Online Birth Certificate Processing System with SMS Notification “ is an IT-based solution that aims to automate the process of requesting, verifying, and approving inquiries for original birth records. The system will eliminate the traditional method and transition the birth certificate processing into an easy, convenient, and efficient manner. The researchers will develop the project following the Software Development Life Cycle (SDLC) technique.

• Food Donation Services Database Design

Information technology plays a significant role in automating the operations of many companies to boost efficiency. One of these is the automation of food donation and distribution management. “Food Donation Services,” the capstone project, is intended to serve as a platform for facilitating transactions between food groups, donors, and recipients. Food banks will be able to respond to various food donations and food assistance requests in a timely and effective manner as a result of the project.

• COVID Profiling Database Design

The capstone project “City COVID-19 Profiling System with Decision Support” is designed to automate the process of profiling COVID-19 patients. The project will empower local health officers in electronically recording and managing COVID-19 patient information such as symptoms, travel history, and other critical details needed to identify patients. Manual profiling is prone to human mistakes, necessitates a lot of paperwork, and needs too much time and effort from the employees.

• Evacuation Center Database Design

Calamities can have a significant impact on society. It may result in an enormous number of people being evacuated. The local government unit assigned evacuation centers to provide temporary shelter for people during disasters. Evacuation centers are provided to give temporary shelter for the people during and after a calamity. Evacuation centers can be churches, sports stadium community centers, and much more that are capable to provide emergency shelter.

• QR Code Fare Payment System Database Design

The capstone project, “QR Code Fare Payment System” is designed to automate the procedure of paying for a fare when riding a vehicle. Passengers will register in the system to receive their own QR code, which they will use to pay for their fares by scanning in the system’s QR code scanning page. The project will enable cashless fare payment.

• Web Based Psychopathology Diagnosis System Database Design

The capstone project entitled “Web-Based Psychopathology Diagnosis System” is designed for patients and medical staff in the field of psychopathology. The system will be a centralized platform to be used by patients and psychopathologists for consultations. The said project will also keep all the records electronically. Mental health is important. Each individual must give importance to their mental health by paying attention to it and seek medical advice if symptoms of mental disorders and unusual behavior occur.

• Service Marketplace System Database Design

The capstone project, “Services Marketplace System” is designed to serve as a centralized platform for marketing and inquiring about different services. The system will serve as a platform where different service providers and customers will have an automated transaction. Technology made it easier for people to accomplish daily tasks and activities. In the conventional method, customers avail themselves of services by visiting the shop that offers their desired services personally.

• Fish Catch System Database Design

The capstone project, entitled “Fish Catch Monitoring System” will automate the process of recording and monitoring fish catches. The said project is intended to be used by fisherman and fish markets to accurately record fish catches and will also keep the records electronically safe and secure.

• Complaints Handling Management System Free Template Database Design

The capstone project, “Complaint Handling Management System” is a system designed to help educational institutions to handle and manage complaints electronically. The system will improve the response time of the school’s management in addressing the complaints of the students, parents, staff, and other stakeholders.

• Senior Citizen Information System Free Template Database Design

The system will replace the manual method of managing information and records of the senior citizen to an electronic one. The system will serve as a repository of the record of the senior citizen within the scope of a specific local government unit. By using the system, paper works will be lessened and human errors in file handling will be avoided. The system is efficient enough to aid in managing and keeping the records of the senior citizens in the different barangay.

• Online and SMS-Based Salary Notification Database Design

The “Online and SMS Based Salary Notification” is a capstone project intended to be used by companies and employees to automate the process of notifying salary details. The application will work by allowing the designated company encoder to encode details of salary and the employees to log in to his/her account in the application and have access to the details of his/her salary. One of the beauties of being employed is being paid. Employers manage the employee’s salary and are responsible to discuss with the employees the system of the salary and deductions.

• Maternal Records Management Database Design

The capstone project, “Maternal Records Management System” is a system that automates the process of recording and keeping maternal records. The said project will allow maternity clinics to track and monitor their patients’ records from pregnancy to their baby’s immunization records.

• Online Complaint Management System Database Design

Online Complaint Management System is a capstone project that is design to serve as a platform to address complaints and resolve disputes. The system provides an online way of resolving problems faced by the public or people within the organization. The system will make complaints easier to coordinate, monitor, track and resolve.

• Online Donation Database Design

The capstone project ,  “Online Donation Platform for DSWD” is an online platform for giving and asking donations in the Department of Social Welfare and Development (DSWD). The system will be managed by the staffs of the DSWD to verify donors and legible beneficiaries electronically. The system will have an SMS feature to notify the donors and beneficiaries about the status of their request.

• OJT Timesheet Monitoring System using QR Code Database Design

The capstone project, “OJT Timesheet Monitoring System using QR Code” allows employer to automate timesheet of each trainee for easy monitoring. The system will be used by the on-the-job trainees to serve as their daily time in and out using the QR code generated by the system. The entire system will be managed by the administrator.

Technology is attributed with driving change in a wide range of enterprises and institutions. Because of information technology, the world has altered dramatically. It is difficult to imagine an industry or organization that has not benefited from technology advances. In these businesses, the most common role of IT has been to automate numerous procedures and transactions in order to increase efficiency and improve people’s overall experience and satisfaction. The aforementioned capstone project ideas will be useful in a range of sectors. It will aid in enhancing operational efficiency as well as the services provided to the project’s users.

You may visit our  Facebook page for more information, inquiries, and comments. Please subscribe also to our YouTube Channel to receive  free capstone projects resources and computer programming tutorials.

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Graduate Tracer System Database Project

CS 764 Topics in Database Management Systems

This course covers a number of advanced topics in the development of database management systems (DBMS) and the modern applications of databases. The topics discussed include query processing and optimization, advanced access methods, advanced concurrency control and recovery, parallel and distributed data systems, cloud computing for data platforms, and data processing with emerging hardware. The course material will be drawn from a number of papers in the database literature. We will cover one paper per lecture. All students are expected to read the paper before coming to the lecture.

Prerequisites: CS 564 or equivalent. If you have concerns about meeting the prerequisties, please contact the instructor.

• Red Book : Readings in Database Systems (5th edition) - edited by Bailis, Hellerstein, and Stonebraker.
• Cow Book : Database Management Systems (3rd edition) - by Raghu Ramakrishnan and Johannes Gehrke, McGraw Hill, 2003.

Lecture Format: Each lecture focuses on a classic or modern research paper. Students will read the paper and submit a review to https://wisc-cs764-f22.hotcrp.com before the lecture starts. Here is a sample review for the paper on join processing.

Course projects: A big component of this course is a research project. For the project, you pick a topic in the area of data management systems, and explore it in depth. Here are lists of suggested project topics created in 2020 , 2021 , and 2022 ; but you are encouraged to select a project outside of the list. The course project is a group project, and each group must be of size 2-4. Please start looking for project partners right away. The course project will include a project proposal, a short presentation at the end of the semester, and a final project report. Here are three sample projects from previous years ( sample1 , sample2 , sample3 ). The presentations are organized as a workshop. DAWN 2019 to have an idea of what it looks like. --> The project has the following deadlines:

• Proposal due: Oct. 24
• Presentation: Dec. 12 & 14
• Paper submission: Dec. 19
• CloudLab: https://www.cloudlab.us/signup.php?pid=NextGenDB (project name: NextGenDB)
• Chameleon: https://www.chameleoncloud.org (project name: ngdb)
• Paper review: 15%
• Project proposal: 10%
• Project presentation: 10%
• Project final report: 30%

How To Select Topics For a Term Paper? Matchmaking With The Subject

Academic worries don’t end with making assignments! These troubles start when you’re assigned a semester project. If you’re unfamiliar with this subject, don’t worry! In this blog, we will uncover the mystery of how to select topics for a term paper. Today, we will talk about all the complexities surrounding the selection process every student will encounter. Bonus point: Our writing professional team will help you sort this problem out. For good! Let’s not waste a single minute and start this journey to excellence.

Table of Contents

Selection Criteria For Term Paper Topics

Before students move to pick a topic for their term project, they must evaluate a few steps. Format of writing a term paper is not as delicate as searching for a topic because it’s your compass. Moving in the right direction is impossible unless you know where your north star is.

Similarly, your topic is the north star that guides you through navigation and helps in reaching the destination safely. Here, we will discuss the steps that you should consider while going through the selection process for your coursework project.

Understand and Know Your Capabilities

Picking a topic is a choice that always rests on a student. Many learners will disagree because your teacher usually assigns the topic regardless. If you’ve received the essay prompt, then you may have already started working on the research. Students still have the choice of picking a topic that helps them hone their individual skills.

If you’re an avid learner who loves to challenge the norms and explore new possibilities, then you should ask for a selection choice. You may ask your professor to allow you to choose a topic that perfectly fits your skills. For example, if you’re a fan of history and Nikola Tesla , but your essay prompt gives a topic such as “ American Civil War ,” then you may ask and discuss your case with the teacher. 

Additionally, topic selection becomes easy when you know your skills and capabilities. Matching your talent with your subject is the perfect recipe for academic writing and its effectiveness. To do this, you must analyze the subjects you’re good at and your interest that makes you unique. A synergy is created when both a writer and the subject match the criteria of individual uniqueness.

You may brainstorm ideas that excite you, let you think critically, and allow sufficient room for creativity. Having a subject based on your choice helps you dig down for more research and new findings. Since it’s your favorite one, you will keep digging for more unique points that an author has not covered before. By knowing your skill sets, you’ll be able to use your abilities in the best way possible in semester papers. 

Don’t Go Cherry Picking: Accept New Challenges

The second step is selecting a topic that challenges you personally and mentally. Cherry picking might offer you a quick and easy solution to the selection process. But it won’t offer new ideas and make you settle for less creativity and repetition of work already done. Students must look outside the box and consider the topic challenging their comfort zone.

Secondly, choosing a subject that’s challenging for you and your understanding allows you to see the picture from a different perspective. Hence, you can increase your knowledge and remove your reservations in one go. A challenging topic allows you to develop skills you haven’t learned yet. So, there’s a pretty good chance you’ll grow your current skills to the level of a professional by picking challenging topics. 

Thirdly, new challenges allow you to grow your initiative skills, which are necessary for self-reliance and confidence. A disputed topic lets you analyze the shortcomings and find their solutions. Your problem-solving skills get rapidly boosted when you choose topics that expand your knowledge about the subject matter. 

Additionally, disputed topics offer a window of opportunity that allows you to make a name for yourself. Controversial subjects are difficult, but they get you the attention you want. Choosing one allows you to investigate them and present your stance that shows your thoughts. Your teacher or professor gets to know you as a writer and not a student; it helps them find areas of improvement in your writing.

Availability of Credible Resources

The third step that’s pivotal to selecting term project topics is choosing a subject with sufficient and credible resources in abundance. As a student, you must choose a topic discussed before with plenty of evidence or arguments available.

Credible sources can be categorized as scholarly articles, journals, interviews, and written or visual aids in favor or against the subject of discussion. Having solid research with sources allows you to present your opinions with confidence and clarity. Furthermore, you can easily build your reputation as an authentic writer with such usage.

Secondly, reading and using authentic sources expands your horizon of knowledge, helping you become an expert in your field of study. With vast information, you can present your arguments thoroughly and defend your stance on the topic.

You may take the following steps that will ensure you end up with authentic data rather than insubstantial evidence.

• For medical research, use “The Lancet”, CMDT (Current Medical Diagnosis & Treatment), NEJM (New England Journal of Medicine), etc.
• Find scientific research journals on Google Scholar.
• Research for interviews of SMEs (Subject Matter Experts) on the topic and note down important information.

You may shorten your research and evidence evaluation process by comparing statements. Choose two or three statements on the topic, and if they all say the same thing, Bingo! You’ve got yourself an evidence-based argument.

Subject Scope and Relevance

The most useful and substantial step is evaluating the scope of the subject and its relevance to your field of study. Before you take the big step, you must evaluate the scope of your term article. Finding or defining the scope allows you to be specific and address the issues beforehand. This action lets you know the parameters and objectives and forecast the results of your course project. 

Scope also informs the researchers what steps they should take to achieve the results or reach a conclusion. A topic with an immense scope but not manageable under certain conditions should be avoided because it will cost you time and budget that you might not be able to spend.

Hence, you must choose a topic for an academic paper according to its scope and nature of research. Additionally, relevancy must be in check because you can’t write something that has no relevance to your studies and your target audience. This step is technical but requires attention to detail before you choose a subject that wastes your time and words on vague statements.

Fueling Audience’s Curiosity

Students must remember to select a topic that fuels the curiosity train of their peers and members of the audience. If you’re not bringing something new to the table, there’s a big chance your paper will end up in the dustbin. You must satisfy the needs of your target audience and educate them on new findings.

Additionally, there’s a chance your audience members might not be familiar with the in-depth nature of your research. Thus, providing them with valuable information spreads intellect and knowledge. It helps strengthen your position in the academic world and allows you to create more informative semester projects to impress them.

Lastly, the more realistic results your assignment paper will contain, the more critical thinking it will promote. The door to impressive paper goes through choosing a topic that justifies the time your reader spends reading it. Adequately satisfying your audience’s thirst for knowledge helps create a difference and makes you a professional.

Fulfilling Academic Requirements

The last step to remember while choosing a topic is analyzing whether your term paper fulfills academic requirements. If your subject is not compounded by academic parameters and devoiding key definitions, then you have a big problem to deal with. 

You must understand what this assignment requires from you. It means informing your audience with your intellect or evaluating phenomena from your eyes. So in both cases, you’ll present an analysis that should contain something useful for the discipline.

If your term paper topics justify the needs and requirements stipulated by the essay prompt or academic guidelines, it’s a green light. You must consult with your professor to clarify and remove ambiguity in this step. Your teachers understand how you can ensure an active role in your academic growth and discipline. Hence, a short or ten-minute conversation will end many assumptions and misunderstandings between academic requirements and topic selection.

So, these steps will help you in the long term and ensure a thorough understanding of the term paper topics and their selection criteria.

Being well-versed in knowing how to select topics for a term paper helps in advancing your academic career and fast-track your journey toward greatness. Follow our expert’s guide and leave your footprint in history.

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200 Term Paper Topics in Different Fields

Table of contents

• Writing Metier

Welcome to a treasure trove of term paper topics thoughtfully crafted by the expert team of term paper writers  at Writing Metier. 

As a co-founder of this dynamic company, I’ve witnessed the struggles many students face when choosing the right topic for their term paper. That’s why we’ve rolled up our sleeves to provide you with an arsenal of ideas that are not only academically enriching but also incredibly engaging.

In this article, you’ll find a rich array of topics to start with while writing your term paper , handpicked to ignite your curiosity and fuel your academic pursuits. 

From the persuasive depth required in argumentative papers to the innovative angles needed for experimental research, our collection is a kaleidoscope of possibilities. 

Whether you’re in search of easy term paper topics to get you over the line or you’re hunting for something more challenging to showcase your analytical prowess, this list is your starting point on the path to academic success.

I’ll break our term paper topic suggestions list into such types:

• Argumentative Papers – c ommon in many disciplines, they develop critical thinking and persuasion skills.
• Analytical Papers –  widely assigned, these papers help students develop analytical and interpretive skills.
• Compare and Contrast Papers –  regularly used across subjects, they teach students to identify similarities and differences in concepts, theories, or works.
• Cause and Effect Papers –  often found in social sciences, they help students understand the relationship between different events or phenomena.
• Definition Papers –  useful in explaining complex concepts or terms, especially in technical or specialized fields.
• Interpretive Papers –  common in literature, history, and arts, these papers require a deep understanding of the material and the ability to interpret underlying meanings.
• Reports –  these are fundamental in many scientific and technical courses, focusing on clear, structured presentation of information.
• Survey Research Papers –  particularly common in social sciences, they involve data collection and analysis skills.
• Experimental Research Papers –  a staple in natural and applied sciences, these papers are crucial for understanding scientific methodologies and processes.
• Review Papers –  often found in graduate studies, they require a comprehensive understanding of existing research in a particular field.

Term Paper Topic and Question Ideas

Let’s begin with what you all have been waiting for – topic suggestions!

Argumentative Term Paper Topics

When it comes to crafting a compelling argumentative term paper, the choice of topic is crucial. In this section, we present some of the best topics for term papers that challenge you to take a stand, defend your viewpoint, and persuade your audience. 

These topics are not just good term paper topics; they are gateways to exploring contemporary issues with depth and clarity.

• Social Media Influence : Does social media do more harm than good in shaping young people’s worldviews?
• Climate Change Policies : Should governments enforce stricter regulations to combat climate change?
• Artificial Intelligence Ethics : Is the rapid development of AI technology a threat to human employment?
• Vaccination Mandates : Should vaccinations be mandatory for public health safety?
• Online Education vs Traditional : Is online education as effective as traditional classroom learning?
• Animal Testing in Research : Should animal testing be banned in scientific research?
• Genetic Engineering : Are the benefits of genetic engineering worth the ethical concerns?
• Privacy in the Digital Age : Is government surveillance a necessary tool for national security or an invasion of privacy?
• Renewable Energy : Should governments invest more in renewable energy sources over fossil fuels?
• Minimum Wage Increase : Does increasing the minimum wage help or hurt the economy?
• Gun Control Laws : Do stricter gun control laws reduce gun violence?
• Legalization of Marijuana : Should marijuana be legalized for recreational use?
• Death Penalty : Is the death penalty an effective deterrent for major crimes?
• School Uniforms : Do school uniforms contribute to a better learning environment?
• Universal Basic Income : Can a universal basic income solve economic inequality?
• Space Exploration Funding : Should space exploration be prioritized over addressing Earth’s issues?
• Plastic Ban : Would a global ban on single-use plastics be environmentally beneficial?
• Affirmative Action in Education : Is affirmative action still necessary in education admissions?
• Euthanasia : Should euthanasia be legalized to allow people with terminal illnesses to die with dignity?
• Censorship in Media : Is censorship necessary to protect society, or does it infringe on freedom of expression?

While argumentative papers test your persuasive skills, the realm of analytical papers requires a different approach. Let’s shift our focus to topics that demand a detailed examination and critical analysis .

Analytical Term Paper Topics

If dissecting complex topics and examining them from multiple angles excites you, our list of analytical term paper topics is tailor-made for you. 

Ranging from easy term paper topics to more intricate ones, these themes allow you to showcase your analytical prowess and turn a critical eye on a variety of subjects.

• Impact of COVID-19 on Global Economy : Analyze the long-term economic effects of the COVID-19 pandemic globally.
• Social Impacts of Remote Work : Examine how remote work has changed social interactions and workplace dynamics.
• Cryptocurrency’s Role in Finance : Analyze the potential impacts of cryptocurrency on traditional banking systems.
• Psychological Effects of Social Media : Evaluate how social media platforms impact mental health and self-esteem.
• Climate Change and Migration : Investigate the relationship between climate change and patterns of human migration.
• Rise of Streaming Services : Analyze the impact of streaming services on the traditional television and movie industries.
• Gender Pay Gap : Examine the factors contributing to the gender pay gap in different industries.
• Artificial Intelligence in Healthcare : Analyze the benefits and challenges of implementing AI in healthcare systems.
• Cybersecurity in the Digital Age : Evaluate the effectiveness of current cybersecurity measures in protecting data privacy.
• Impact of Electric Vehicles on the Auto Industry : Investigate how electric vehicles are reshaping the future of the automotive industry.
• Effects of Urbanization on Environment : Analyze the environmental consequences of rapid urbanization.
• Mental Health in the Workplace : Examine the role of workplace culture in employee mental health and wellbeing.
• Renewable Energy’s Economic Feasibility : Analyze the economic sustainability of transitioning to renewable energy sources.
• Influence of Advertising on Consumer Behavior : Evaluate how advertising strategies affect consumer choices and spending habits.
• Gentrification and Community Displacement : Investigate the social and economic impacts of gentrification on local communities.
• Sustainable Fashion Industry : Analyze the challenges and opportunities in making the fashion industry more sustainable.
• Impact of Globalization on Local Cultures : Examine how globalization affects the preservation and evolution of local cultural identities.
• E-Sports and Gaming Culture : Analyze the rise of e-sports and its impact on mainstream sports and entertainment.
• Food Security and Climate Change : Investigate the relationship between climate change and global food security challenges.
• Technology and Education Reform : Analyze how technological advancements are shaping modern education methods and accessibility.

From the precision of analysis, we now move to the art of comparison and contrast, where you will juxtapose differing views or phenomena to shed new light on your subject.

Compare and Contrast Term Paper Topics

Finding the perfect balance between two subjects is at the heart of a great compare and contrast term paper . 

This section offers a range of topics that serve as excellent examples of term paper topics, inviting you to explore and contrast diverse ideas, theories, or events, enriching your understanding of both.

• Online Learning vs. Traditional Classroom : Compare and contrast the effectiveness of online learning with traditional classroom settings.
• Capitalism vs. Socialism : Analyze the advantages and disadvantages of capitalism compared to socialism.
• Renewable Energy vs. Fossil Fuels : Compare the environmental impacts and sustainability of renewable energy sources versus fossil fuels.
• Modern Art vs. Classical Art : Contrast the themes and techniques of modern art with those of classical art.
• Public Healthcare vs. Private Healthcare : Compare the efficiency and quality of public healthcare systems to private healthcare systems.
• E-books vs. Printed Books : Analyze the differences in user experience and environmental impact between e-books and traditional printed books.
• Western Diet vs. Mediterranean Diet : Contrast the health impacts of a typical Western diet with the Mediterranean diet.
• Android vs. iOS : Compare the functionality, user interface, and customization options of Android and iOS platforms.
• Traditional Marketing vs. Digital Marketing : Analyze the effectiveness and reach of traditional marketing methods compared to digital marketing strategies.
• Democracy vs. Authoritarianism : Contrast the political, social, and economic outcomes in democratic versus authoritarian regimes.
• Organic Farming vs. Conventional Farming : Compare the environmental impact and productivity of organic farming methods to conventional farming practices.
• Freudian Psychoanalysis vs. Cognitive Behavioral Therapy : Analyze the methodologies and effectiveness of Freudian psychoanalysis compared to cognitive behavioral therapy.
• Remote Work vs. Office Work : Contrast the impacts on productivity and work-life balance between remote work and traditional office settings.
• Vegetarian Diet vs. Meat-Inclusive Diet : Compare the health benefits and environmental impacts of a vegetarian diet versus a diet that includes meat.
• Classical Music vs. Pop Music : Analyze the differences in structure, audience, and cultural impact between classical music and contemporary pop music.
• Electric Cars vs. Gasoline Cars : Contrast the environmental impact, cost, and performance of electric cars with traditional gasoline cars.
• Public Schools vs. Private Schools : Compare the educational outcomes and resources available in public schools versus private schools.
• Social Networking vs. Traditional Networking : Analyze the effectiveness and depth of connections made through social networking sites compared to traditional networking methods.
• Modern Architecture vs. Gothic Architecture : Contrast the design principles, materials, and cultural significance of modern architecture with that of Gothic architecture.
• Globalization vs. Nationalism : Compare the economic, cultural, and political impacts of globalization against the principles of nationalism.

As we transition from the balance of comparison to the cause and effect dynamics, prepare to delve into topics that explore the relationship between various factors and their consequences.

Cause and Effect Term Paper Topics

Understanding the intricate relationship between cause and effect is essential for any academic exploration. 

This list provides a range of interesting term paper topics that will help you unravel the connections between causes and their subsequent effects, offering a fascinating journey into the why and how of various phenomena.

• Technology Advancements and Job Market : Analyze the effect of technological advancements on the job market and employment trends.
• Global Warming and Weather Patterns : Examine the causal relationship between global warming and extreme weather patterns.
• Social Media and Youth Mental Health : Investigate the effects of social media usage on the mental health of young people.
• Economic Recession and Small Businesses : Analyze the impact of economic recessions on the survival and growth of small businesses.
• Urbanization and Wildlife Habitats : Explore the effects of urbanization on local wildlife habitats and biodiversity.
• Education System Reforms and Student Performance : Examine how recent reforms in the education system have impacted student performance and learning outcomes.
• Parenting Styles and Child Development : Investigate the effect of different parenting styles on the emotional and psychological development of children.
• Air Pollution and Respiratory Health : Analyze the causal relationship between air pollution levels and respiratory health issues in urban areas.
• Diet and Physical Health : Examine the effects of different dietary habits on physical health and wellness.
• Political Policies and Economic Growth : Investigate the impact of various political policies on a country’s economic growth and stability.
• Stress and Workplace Productivity : Analyze the effects of workplace stress on employee productivity and job satisfaction.
• Deforestation and Climate Change : Explore the causal relationship between deforestation and climate change.
• Exercise and Mental Wellbeing : Examine the effect of regular physical exercise on mental health and mood stability.
• Consumer Behavior and Marketing Strategies : Investigate how marketing strategies cause shifts in consumer buying behavior.
• Immigration Policies and Labor Markets : Analyze the impact of immigration policies on the labor market and workforce demographics.
• Mobile Technology and Social Interaction : Examine the effects of mobile technology on face-to-face social interactions and communication.
• Sleep Patterns and Academic Performance : Investigate the causal relationship between sleep patterns and academic performance in students.
• Cultural Globalization and National Identities : Analyze the effect of cultural globalization on the preservation of national identities and traditions.
• Government Regulations and Entrepreneurship : Examine the impact of government regulations on entrepreneurship and business innovation.
• Online Retail and Brick-and-Mortar Stores : Explore the effects of the rise of online retail on traditional brick-and-mortar stores.

Moving from the causality of events, let’s turn our attention to the essence of specific concepts and ideas, where definition term papers allow you to articulate and explore in-depth.

Definition Term Paper Topics

The art of defining a concept or a term goes beyond mere description. 

In this section, you’ll find term paper topics for students that revolve around defining and explicating complex ideas or phenomena, challenging you to crystallize your understanding into clear, concise language.

• Defining Artificial Intelligence : Explore the various dimensions and implications of artificial intelligence in the modern world.
• Understanding Blockchain Technology : Provide a comprehensive definition and examine the potential impacts of blockchain technology.
• Concept of Sustainability : Define sustainability in the context of environmental, economic, and social dimensions.
• Defining Modern Feminism : Explore the evolution and current meaning of feminism in contemporary society.
• Understanding Cultural Appropriation : Define cultural appropriation and discuss its implications in arts, fashion, and media.
• The Gig Economy : Provide a detailed definition and explore the rise and impact of the gig economy on traditional work structures.
• Defining Cybersecurity : Examine the scope and importance of cybersecurity in the digital age.
• Understanding Genetically Modified Organisms (GMOs) : Define GMOs and discuss their role and controversy in food production.
• Concept of Globalization : Offer a comprehensive definition and explore the multifaceted impacts of globalization.
• Mental Health Awareness : Define mental health and discuss the importance of awareness and de-stigmatization in society.
• Defining Renewable Energy : Explore the concept of renewable energy and its role in combating climate change.
• The Digital Divide : Define the digital divide and examine its implications in various socio-economic contexts.
• Understanding Euthanasia : Provide a comprehensive definition and discuss the ethical implications of euthanasia.
• Concept of Social Justice : Define social justice and explore its significance in modern societal structures.
• Understanding Quantum Computing : Define quantum computing and discuss its potential impacts on the future of technology.
• Defining Urbanization : Explore the process of urbanization and its impacts on societies and environments.
• Concept of Virtual Reality : Provide a comprehensive definition and explore the applications and implications of virtual reality.
• Understanding Nutrigenomics : Define nutrigenomics and discuss its role in personalized nutrition and health.
• Defining Corporate Social Responsibility (CSR) : Examine the concept of CSR and its importance in the modern business world.
• Understanding Telemedicine : Define telemedicine and discuss its growing role in the healthcare system.

With definitions well in hand, we now venture into the territory of interpretive term papers, where your insight and personal perspective bring unique interpretations to the forefront.

Interpretive Term Paper Topics

Interpretive term papers provide a canvas for your thoughts and analyses, allowing you to delve into texts, artworks, or phenomena with a subjective lens. 

Here, we offer term paper topics easy for engagement yet rich in potential for deep, personal interpretation, perfect for those looking to add their unique voice to academic discourse .

• Interpreting Shakespeare’s Hamlet : Explore the themes of madness, revenge, and morality in Shakespeare’s “Hamlet”.
• The Great Gatsby and the American Dream : Interpret F. Scott Fitzgerald’s representation of the American Dream in “The Great Gatsby”.
• Picasso’s Guernica : Analyze the symbolism and political commentary in Picasso’s masterpiece “Guernica”.
• Orwell’s 1984 and Modern Surveillance : Interpret the relevance of George Orwell’s “1984” in the context of today’s digital surveillance society.
• Symbolism in Dante’s Inferno : Explore the use of symbolism in Dante Alighieri’s “Inferno” and its representation of sin and redemption.
• Jane Austen’s Critique of Social Class : Interpret the social class critique in Jane Austen’s “Pride and Prejudice”.
• Van Gogh’s Starry Night and Emotional Expression : Analyze the expression of emotion and meaning in Vincent van Gogh’s “Starry Night”.
• To Kill a Mockingbird and Racial Injustice : Interpret Harper Lee’s depiction of racial injustice and moral growth in “To Kill a Mockingbird”.
• Beethoven’s Symphony No. 9 and Its Historical Context : Analyze the historical context and musical innovation of Beethoven’s Symphony No. 9.
• Franz Kafka’s The Metamorphosis and Alienation : Explore themes of alienation and identity in Franz Kafka’s “The Metamorphosis”.
• The Symbolism in Salvador Dali’s Surrealist Art : Interpret the symbolism and psychological depth in Salvador Dali’s surrealist paintings.
• Homer’s Odyssey and the Hero’s Journey : Analyze the elements of the hero’s journey in Homer’s “Odyssey”.
• Frida Kahlo’s Self-Portraits and Personal Struggle : Interpret the expression of personal struggle and identity in Frida Kahlo’s self-portraits.
• Mark Twain’s Satire in Huckleberry Finn : Analyze Mark Twain’s use of satire in “The Adventures of Huckleberry Finn” to critique society.
• The Tragic Hero in Sophocles’ Oedipus Rex : Interpret the concept of the tragic hero in Sophocles’ “Oedipus Rex”.
• Joyce’s Ulysses and Stream of Consciousness : Explore the use of stream of consciousness in James Joyce’s “Ulysses”.
• Tolkien’s The Lord of the Rings and Mythology : Analyze J.R.R. Tolkien’s use of mythology and folklore in “The Lord of the Rings”.
• Michelangelo’s David and Renaissance Ideals : Interpret the representation of Renaissance ideals in Michelangelo’s statue of David.
• Emily Dickinson’s Poetry and Themes of Death : Explore the recurring themes of death and immortality in Emily Dickinson’s poetry.
• The Matrix and Philosophical Symbolism : Analyze the philosophical themes and symbolism in the film “The Matrix”.

From the subjective nuances of interpretation, we shift gears to the objective and structured world of report papers, focusing on presenting information in a clear, organized manner.

Report Term Paper Topics

Report term papers demand precision, structure, and clarity in presenting information and analysis. 

This section provides you with a range of term paper research topics that are ideal for crafting detailed and informative reports, covering a spectrum of subjects that are both current and engaging.

• COVID-19’s Impact on Global Health Systems : Report on how different health systems worldwide have responded to the COVID-19 pandemic.
• Renewable Energy Progress Report : Analyze the current state and future prospects of renewable energy technologies globally.
• Urbanization and Its Environmental Impact : Report on the environmental consequences of rapid urbanization in major cities.
• Technological Advancements in Education : Explore the integration and impact of technology in modern educational systems.
• Climate Change and Agricultural Practices : Analyze how climate change is affecting agricultural practices and food production.
• Mental Health Services Accessibility : Report on the accessibility and quality of mental health services in various countries.
• Consumer Trends in E-commerce : Analyze the evolving consumer behavior trends in the e-commerce industry.
• Public Transportation Systems Comparison : Compare and evaluate public transportation systems across major global cities.
• Plastic Pollution and Marine Life : Report on the impact of plastic pollution on marine ecosystems and biodiversity.
• Digital Divide and Internet Access : Explore the current state of the digital divide and its impact on internet access globally.
• Advancements in Cancer Research : Report on the latest developments and challenges in cancer research and treatment.
• Impact of Social Media on Politics : Analyze how social media platforms are influencing political discourse and elections.
• Sustainable Tourism Practices : Report on the adoption and effectiveness of sustainable practices in the tourism industry.
• Artificial Intelligence in Business : Explore the use and impact of AI technologies in various business sectors.
• Effects of Urban Green Spaces : Analyze the social and environmental effects of green spaces in urban areas.
• Childhood Obesity Trends and Policies : Report on the trends and public health policies addressing childhood obesity.
• Gender Equality in the Workforce : Analyze the progress and challenges of achieving gender equality in the workplace.
• Impact of GMOs on Agriculture and Environment : Report on the benefits and risks associated with the use of GMOs in agriculture.
• Cybersecurity Threats and Prevention Measures : Explore current cybersecurity threats and the effectiveness of various prevention strategies.
• Affordable Housing Crisis Analysis : Report on the state of the affordable housing crisis and the effectiveness of measures taken to address it.

As we move from structured reports to the dynamic world of survey research, prepare to engage with topics that require you to gather and analyze data from real-world sources.

Survey Research Term Paper Topics

Survey research term papers are your gateway to exploring the opinions, behaviors, and trends that shape our world. 

This list of term paper topics help you design, conduct, and analyze surveys, providing valuable insights into various aspects of human behavior and societal trends.

• Student Perceptions of Online Learning : Conduct a survey to understand student experiences and perceptions of online learning environments.
• Consumer Attitudes Towards Green Products : Survey consumer attitudes and purchasing behaviors regarding environmentally friendly products.
• Impact of Work-from-Home on Productivity : Survey employees across different sectors to analyze the impact of remote work on productivity.
• Social Media’s Role in Mental Health : Conduct a survey to explore the relationship between social media use and mental health among adolescents.
• Public Opinion on Climate Change Policies : Survey public opinion on various climate change policies and their perceived effectiveness.
• Healthcare Accessibility and Satisfaction : Conduct a survey to assess public satisfaction with healthcare services and accessibility.
• Attitudes Toward Vaccination in Different Communities : Survey different communities to understand attitudes towards vaccinations and their reasons.
• Consumer Behavior in the Fashion Industry : Conduct a survey to analyze consumer buying patterns and trends in the fashion industry.
• Employee Satisfaction and Workplace Culture : Survey employees in various organizations to assess the impact of workplace culture on job satisfaction.
• Dietary Habits and Health Outcomes : Conduct a survey to explore the relationship between dietary habits and health outcomes.
• Public Perception of Police and Law Enforcement : Survey the public’s perception and trust in police and law enforcement agencies.
• Influence of Influencer Marketing on Purchasing Decisions : Survey consumers to analyze the impact of influencer marketing on their purchasing decisions.
• Attitudes Towards Renewable Energy Adoption : Conduct a survey to understand public attitudes and barriers to adopting renewable energy sources.
• Effects of Music on Concentration and Productivity : Survey a group of individuals to assess how different genres of music affect their concentration and productivity.
• Cultural Participation and Its Social Impact : Conduct a survey to explore the impact of cultural participation on social cohesion and community engagement.
• Perceptions of Online Privacy and Data Security : Survey internet users to understand their concerns and perceptions about online privacy and data security.
• Trends in Fitness and Exercise Habits : Conduct a survey to analyze current trends and attitudes towards fitness and exercise routines.
• Reading Habits and Preferences : Survey a demographic to understand their reading habits and preferences in the digital age.
• Public Attitudes Towards Space Exploration : Conduct a survey to gauge public interest and opinions on space exploration and its funding.
• Consumer Attitudes Towards Sustainable Packaging : Survey consumer opinions and behaviors related to sustainable packaging and its importance.

From the practical applications of survey research, we now dive into the experimental realm, where hypotheses and scientific methods lead the way.

Experimental Research Term Paper Topics

For those fascinated by the scientific method, this collection of experimental research term paper topics offers a playground of inquiry and discovery. 

These topics encourage you to design experiments, test hypotheses, and explore the intricacies of scientific phenomena, making them some of the best topics for term paper in English for aspiring scientists.

• Effect of Light on Plant Growth : Conduct an experiment to determine how different light conditions affect the growth rate of plants.
• Memory Recall in Different Environments : Investigate how environmental factors influence memory recall in individuals.
• Water Quality and Plant Health : Experiment to analyze the effects of various water qualities on the health of a specific plant species.
• Caffeine’s Effect on Cognitive Performance : Conduct a study to assess how caffeine consumption impacts cognitive tasks and reaction times.
• Behavioral Changes in Animals Due to Environmental Stimuli : Observe and record behavioral changes in animals in response to different environmental stimuli.
• Impact of Diet on Athletic Performance : Experiment to evaluate how different diets affect the physical performance of athletes.
• Air Pollution’s Effect on Respiratory Health : Conduct an experiment to explore the impacts of air pollution on respiratory health indicators.
• Sound Frequencies and Plant Growth : Investigate the effect of different sound frequencies on the growth rate of plants.
• Sleep Patterns and Academic Performance : Study the correlation between varying sleep patterns and academic performance in students.
• Effectiveness of Natural vs. Chemical Fertilizers : Experiment to compare the effectiveness of natural and chemical fertilizers on plant growth.
• Temperature Effects on Battery Performance : Assess how different temperatures affect the performance and efficiency of various types of batteries.
• Social Media Use and Attention Span : Conduct a study to explore the relationship between social media usage and attention span in individuals.
• Impact of Exercise on Mental Health : Experiment to analyze the effects of regular physical exercise on mental health and stress levels.
• Plastic Degradation in Different Environments : Investigate the rate of plastic degradation in various environmental conditions.
• Influence of Music on Cognitive Task Performance : Study how listening to different genres of music affects performance on cognitive tasks.
• Effects of Urban Noise on Bird Behavior : Observe and record changes in bird behavior and communication in urban environments with high noise levels.
• Antibacterial Properties of Natural Substances : Experiment to evaluate the antibacterial properties of various natural substances.
• Color Psychology and Consumer Behavior : Study how different colors influence consumer behavior and decision-making in marketing.
• Effect of Video Games on Reflexes and Decision Making : Assess the impact of playing video games on the reflexes and decision-making skills of individuals.
• Microplastics’ Impact on Marine Life : Conduct an experiment to observe the effects of microplastics on the health and behavior of marine organisms.

Finally, we arrive at review papers, where synthesizing and critiquing existing literature becomes your path to academic exploration.

Review Term Paper Topics

Review term papers are an opportunity to engage with and reflect upon existing literature in a meaningful way. 

This list offers a variety of term paper title ideas, inviting you to synthesize, critique, and discuss existing research and literature, placing you at the heart of the academic conversation.

• Literature Review on Climate Change Mitigation Strategies : Review and synthesize current research on various strategies to mitigate climate change.
• Technological Advancements in Renewable Energy : A review of the latest technological innovations in renewable energy and their potential impacts.
• Review of Mental Health Interventions in Schools : Evaluate the effectiveness of different mental health interventions implemented in educational settings.
• Impact of Social Media on Society : A comprehensive review of research examining the social, psychological, and cultural impacts of social media.
• Economic Consequences of Global Pandemics : Review the economic impacts of global pandemics, with a focus on COVID-19.
• Advancements in Artificial Intelligence and Ethics : Analyze current literature on the advancements in AI and the surrounding ethical considerations.
• Sustainable Urban Planning Practices : Review of sustainable urban planning strategies and their effectiveness in various global cities.
• Trends in Global Obesity and Public Health Strategies : Synthesize research on the trends in global obesity and evaluate public health strategies.
• Evolution of Cybersecurity Threats and Defenses : A review of how cybersecurity threats have evolved over time and the responses developed.
• Nutritional Science and Chronic Disease Prevention : Review current research on the role of nutrition in preventing chronic diseases.
• The Psychology of Advertising : Analyze literature on how advertising tactics psychologically influence consumer behavior.
• Innovations in Water Purification Technology : Review recent advancements in water purification technologies and their global implications.
• Impact of Autonomous Vehicles on Transportation : Evaluate research on the potential impacts of autonomous vehicles on transportation systems.
• The Role of Microfinance in Poverty Reduction : Review the effectiveness of microfinance initiatives in reducing poverty in various regions.
• Developments in Cancer Treatment : Synthesize recent developments in cancer treatment, including breakthrough therapies and drugs.
• The Effectiveness of Renewable Energy Subsidies : Review the economic and environmental impacts of subsidies for renewable energy sources.
• Mental Health Effects of Climate Change : Analyze literature on the psychological effects of climate change on different populations.
• Blockchain Technology and Financial Services : Review the implications of blockchain technology in reshaping financial services.
• Genetic Engineering in Agriculture : Evaluate the benefits and risks associated with the use of genetic engineering in agriculture.
• Telemedicine and Healthcare Accessibility : Review the impact of telemedicine on improving healthcare accessibility and efficiency.

As you reach the end of this list, remember that choosing the right topic is the first step in the dance of academic writing. Each topic here is a window into a new world of ideas and discoveries, waiting for you to open it. 

Your term paper is an opportunity to not just meet academic expectations, but to express your thoughts, analyze critically , and contribute to a broader conversation in your field. 

If you need assistance with more specific topic suggestions from our experts, you can fill out our “Free topic suggestions” form. Moreover, our term paper writers are at your service if you need writing or editing assistance.

So, pick a topic that resonates with you, let your curiosity guide your research, and create a term paper that stands out. At Writing Metier , we’re excited to see where your choice will take you, and we’re here to support you every step of the way.

Free topic suggestions

Vasy kafidoff.

Vasyl Kafidoff is a co-founder and CEO at WritingMetier. He is interested in education and how modern technology makes it more accessible. He wants to bring awareness about new learning possibilities as an educational specialist. When Vasy is not working, he’s found behind a drum kit.

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500 Good Research Paper Topics

Bonus Material: Essential essay checklist

Writing a research paper for a class and not sure how to start?

One of the most important steps to creating a great paper is finding a good topic! 

Here’s a hand-drafted list from a Princeton grad who has helped professors at Harvard and Yale edit their papers for publication and taught college writing at the University of Notre Dame and .

What’s more, we give you some foolproof formulas for creating your own paper topic to fit the requirements of your class.

Using these simple formulas, we’ve helped hundreds of students turn a B- paper topic into an A+ paper topic.

Keep reading for our list of 500 vetted research paper topics and our magic formulas for creating your own topic!

Of course, if you want help learning to write research papers tailored to your individual needs, check out our one-on-one writing coaching or academic writing workshop . Set up a free consultation to see how we can help you learn to write A+ papers!

Jump to paper topics in:

European & Mediterranean History

African history, asian history, history of the pre-columbian americas.

• Latin American History

History of Science

Politics & public policy, education & education policy, political theory, science policy.

• Health Sciences & Psychology

What is a research paper?

In order to write a good research paper, it’s important to know what it is! 

In general, we can divide academic writing into three broad categories:

• Analytical: analyze the tools an author uses to make their point
• Research: delve deeply into a research topic and share your findings
• Persuasive : argue a specific and nuanced position backed by evidence

What’s the difference between an analytical paper and a research paper? For an analytical paper, it’s okay to just use one or two sources (a book, poem, work of art, piece of music, etc.) and examine them in detail. For a research paper, however, the expectation is that you do, well . . . research .

The depth of research that you’re expected to do will depend on your age and the type of class you’re taking.

In elementary or middle school, a “research paper” might mean finding information from a few general books or encyclopedias in your school library. 

In high school, your teachers might expect you to start using information from academic articles and more specific books. You might use encyclopedias and general works as a starting point, but you’ll be expected to go beyond them and do more work to synthesize information from different perspectives or different types of sources. You may also be expected to do “primary research,” where you study the source material yourself, instead of synthesizing what other people have written about the source material.

In college, you’ll be required to use academic journals and scholarly books, and your professors will now expect that you be more critical of these secondary sources, noticing the methodology and perspectives of whatever articles and books you’re using. 

In more advanced college courses, you’ll be expected to do more exhaustive surveys of the existing literature on a topic. You’ll need to conduct primary research that makes an original contribution to the field—the kind that could be published in a journal article itself.

For a walkthrough of the 12 essential steps to writing a good paper, check out our step-by-step guide .

Working on a research paper? Grab our free checklist to make sure your essay has everything it needs to earn an A grade.

Get the essential essay checklist

What makes a good research paper topic?

One of the most important features of a research paper topic is that it has a clear, narrow focus. 

For example, your teacher may assign you to write a research paper related to the US Revolutionary War. Does that mean that your topic should be “the US Revolutionary War”? 

Definitely not! There’s no way to craft a good paper with in-depth research with such a broad topic. (Unless you’re in elementary or middle school, in which case it’s okay to have a more general topic for your research paper.)

Instead, you need to find a more specific topic within this broader one. There are endless ways that you can make this narrower! Some ideas generated from this one broader topic might be:

• Causes of the US Revolutionary War
• Changes in military strategy during the Revolutionary War
• The experiences of Loyalists to England who remained in the American colonies during the Revolutionary War
• How the Revolutionary War was pivotal for the career of Alexander Hamilton
• The role of alliances with France during the US Revolutionary War
• The experiences of people of color during the Revolutionary War
• How George Washington’s previous military career paved the way for his leadership in the Revolutionary War
• The main types of weaponry during the Revolutionary War
• Changes in clothing and fashion over the courses of the Revolutionary War
• How Valley Forge was a key moment in the Revolutionary War
• How women contributed to the Revolutionary War
• What happened in Amherst, Massachusetts during the Revolutionary War
• Field medicine during the Revolutionary War
• How the Battle of Saratoga was a turning point in the Revolutionary War
• How different opinions about the Revolutionary War were reflected in poetry written during that time
• Debates over abolition during the Revolutionary War
• The importance of supply chains during the Revolutionary War
• Reactions to the US Revolutionary war in Europe
• How the US Revolutionary war impacted political theory in England and France
• Similarities and differences between the US Revolutionary War and the French Revolution
• Famous paintings inspired by the US Revolutionary War
• Different ways that the US Revolutionary War has been depicted in modern contemporary culture
• The appropriation of the “Boston Tea Party” by US politicians in the 2010s

This list could go on forever!

In fact, any of these topics could become even more specific. For example, check out the evolution of this topic:

• Economic causes of the Revolutionary war
• The way that tax policies helped lead to the Revolutionary War
• How tax laws enacted 1763–1775 helped lead to the Revolutionary War
• How the tax-free status of the British East India Company helped lead to the Revolutionary War
• How the 1773 tax-free status of the British East India Company helped lead to the Revolutionary War, as reflected in letters written 1767–1775
• How the 1773 tax-free status of the British East India Company helped lead to the Revolutionary War, as reflected in letters written by members of the Sons of Liberty 1767–1775

As you advance in your educational career, you’ll need to make your topic more and more specific. Steps 1–3 of this topic might be okay in high school, but for a college research paper steps 4–7 would be more appropriate!

As you craft your research paper topic, you should also keep in mind the availability of research materials on your subject. There are millions of topics that would make interesting research papers, but for which you yourself might not be able to investigate with the primary and secondary sources to which you have access.

Access to research materials might look like:

• To the best of our knowledge, the sources exist somewhere
• The source isn’t behind a paywall (or you or your school can pay for it)
• Your school or local library has a copy of the source
• Your school or local library can order a copy of the source for you
• The source is in a language that you speak
• The source has been published already (there’s tons of amazing research that hasn’t been published yet, a frustrating problem!)
• You can access the archive, museum, or database where the primary source is held—this might mean online access or travel! To access a source in an archive or museum you’ll often need permission, which often requires a letter of support from your school.

If you’re not sure about access to source materials, talk to a librarian! They’re professionals for this question.

Finally, pick a research topic that interests you! Given that there are unlimited research topics in the world and many ways to adapt a broad topic, there should absolutely be a way to modify a research topic to fit your interests.

Want help learning to write an amazing research paper? Work one-on-one with an experienced Ivy-League tutor to improve your writing skills or sign up for our bestselling academic writing workshop .

Insider tips to generate your own research paper topic

Use these formulas to generate your own research paper topics:

• How did X change over a period of time (year, decade, century)?
• What is the impact (or consequences) of X?
• What led to X?
• What is the role of X in Y?
• How did X influence Y?
• How did X become Y?
• How was X different from Y?
• How is X an example of Y?
• How did X affect Y?
• What were some reactions to X?
• What are the most effective policies to produce X result?
• What are some risks of X?
• How is our current understanding of X incorrect? (advanced)
• What happens if we look at X through the lens of Y theory or perspective? (advanced)

A good research paper topic often starts with the question words—why, how, what, who, and where. Remember to make it as specific as possible!

Good research paper topics

These research paper topics have been vetted by a Princeton grad and academic book editor!

• How did European rivalries (British vs French) impact North American history?
• What was the role of British and French alliances with indigneous tribes during the Seven Years’ War?
• Reactions to the 1754 Albany Congress among North American intellectual figures
• How the Albany Plan served as a model for future attempts at union among the North American colonies
• How did different religious identities (Calvinist, Catholic, etc.) play a role in the aftermath of the Seven Years’ War?
• What were the consequences of the 1763 Treaty of Paris?
• How did the Seven Years’ War impact British debt and colonial economics?
• What were some causes of the US Revolutionary War?
• How did military strategy change during the Revolutionary War?
• What were the experiences of Loyalists to England who remained in the American colonies during the Revolutionary War?
• How was the Revolutionary War pivotal for the career of Alexander Hamilton?
• What was the role of alliances with France during the US Revolutionary War?
• What were the experiences of people of color during the Revolutionary War?
• How did George Washington’s previous military career pave the way for his leadership in the Revolutionary War?
• What were the main types of weaponry during the Revolutionary War? How did that affect the options for military strategies?
• How did clothing and fashion change over the courses of the Revolutionary War?
• How was Valley Forge a key moment in the Revolutionary War?
• How did women contribute to the Revolutionary War?
• What happened in Amherst, Massachusetts (or any other specific location) during the Revolutionary War?
• What was field medicine like during the Revolutionary War? 
• How was the Battle of Saratoga a turning point in the Revolutionary War?
• How were different opinions about the Revolutionary War reflected in poetry written during that time?
• What were the debates over abolition during the Revolutionary War?
• What was the role of supply chains during the Revolutionary War?
• What were reactions to the US Revolutionary war like in Europe? What does that tell us about politics in England, France, the Netherlands, etc?
• How did the US Revolutionary war impact political theory in England and France?
• What are similarities and differences between the US Revolutionary War and the French Revolution?
• What are some famous paintings inspired by the US Revolutionary War? What do differences between these paintings tell us about how the artists who created them saw the war?
• What are some different ways that the US Revolutionary War has been depicted in modern contemporary culture? What does that tell us?
• How was the story of the “Boston Tea Party” appropriated by US politicians in the 2010s, and why?
• What was the difference between the Federalists and the Jeffersonians?
• How did the 1797 XYZ Affair lead to the Quasi-War with France?
• How were loans from European countries and companies (France, Spain, Dutch bankers) key to the early US?
• What were reactions to the Constitutional Convention of 1787?
• Why did the US remain neutral during the French Revolution?
• How did the Alien and Sedition acts contribute to the election of Thomas Jefferson as president?
• What was the US’s reaction to the Haitian revolution? Why did the US not recognize Haitian independence until 1862?
• What were the reactions to John Jay’s Treaty of 1794?
• How have the remarks made by George Washington in his Farewell Address inspired isolationist policies?
• How did interpretations of the Monroe Doctrine change over the decades since its creation? 
• How did the Roosevelt Corollary and Lodge Corollary change and expand the Monroe Doctrine?
• How did the presence of US companies like the United Fruit Company affect US military interventions in Latin America? 
• How was the Monroe Doctrine invoked in the Cuban Missile Crisis of 1962? 
• How was US culture shaped by the Cold War?
• How did ecology play a role in the rise of Ancient Egypt?
• How did water management technologies impact Ancient Egypt?
• How did bureaucracies function in Ancient Egypt?
• How did Egyptian art influence Ancient Greek art?
• Who could be a citizen in Athens in the 5th century BCE? What does this tell us about classical Athenian society?
• What was the impact of the Peloponnesian War?
• What was the impact of Alexander the Great’s attempt to create an empire?
• How does the way that Alexander the Great is represented in art demonstrate conceptions about the relationship between the human and the divine?
• Was there a conception of race in the ancient world? How were these ideas different from our own modern conceptions of race?
• What was the role of debt slavery in the Roman republic? How were these policies ended, and what is the significance of the end of debt slavery? What kinds of slavery remained?
• To what degree does the movie Gladiator accurately the Roman Empire in 176–192 CE?
• What was the role of slavery in managing the large latifundia ?
• How and why did the emperor Constantine I adopt Christianity?
• How did patterns of urbanism in the latter Roman empire change? What does this tell us about challenges being faced at that time?
• What do reactions to the Byzantine empress Theodora tell us about ideas of gender in 6th-century Byzantium?
• How did scientific advancements in Islamic Spain influence the rest of Europe?
• What was the relationship between Muslim, Christian, and Jewish populations in Islamic Spain? How does this compare to the experience of Muslim and Jewish populations in Christian Spain?
• How did medieval troubadour poetry represent a new idea of romantic relationships?
• What are similarities and differences between medieval troubadour poetry and lyric poetry in Ancient Greece? 
• What do letters between women and popes tell us about gender, power, and religion in medieval Europe?
• In what ways was Hildegard of Bingen groundbreaking for her time?
• Who produced beer in medieval England, and what does this tell us about society?
• How did the adoption of hops affect the production and distribution of beer?
• How did beer production allow some women a way to be financially independent?
• How was clothing used to mark religious and cultural identities in 15th- and 16th-century Spain?
• How did print culture change relationships and courting in Georgian England?
• How did churches function as social gathering spaces in Georgian England?
• To what degree is Netflix’s Bridgerton series historically accurate?
• How did ideas of love change in the 18th century? How did philosophy play a role in this?
• When were Valentine cards first commercially available? What does that show us about cultural ideas of love and courtship?
• What were the consequences of the desertification of the Sahara?
• How did trade links on the Red Sea influence Nubian culture?
• How did Carthage build power in Northern Africa around 600–500 BCE?
• What was the impact of the Mercenary War (241–238 BCE) in Carthage?
• How did the Roman province of Africa play a key role in financing the Roman Empire?
• What were the consequences of the Donatist division in the 300s in Northern Africa?
• What was the impact of the large-scale movement of Bedouins from the Arabian peninsula into the Maghreb?
• How was Mande society organized in the Mali Empire? 
• What was the role of the book trade in Timbuktu? What does this tell us about culture and learning in the Mali Empire?
• How did Aksum use trade to build wealth and power? 
• What do Nok terracotta sculptures tell us about Nok culture?
• How did the Luba Empire create a centralized political system? How did the idea of spiritual kins ( balopwe ) play a role in this system?
• How did tax collection work in the Lunda empire?
• What does it mean to say that the Ajuran Empire was a hydraulic empire? How did control over water resources allow the Ajuran Empire to build and consolidate power?
• What is the significance of diplomatic ties between the Somai Ajuran Empire and Ming dynasty China? 
• How did the tribute system in the Kingdom of Kongo help to stimulate interregional trade?
• What was the impact of the introduction of maize and cassava to the Kingdom of Kongo?
• How did women wield influence in the Kingdom of Benin?
• How did the Industrial Revolution in Europe help lead to the Scramble for Africa 1878–1898?
• What were the consequences of the Second Boer War?
• What happened in the Year of Africa (1960)?
• How did the Han dynasty consolidate power in frontier regions? 
• How and why did the Han dynasty nationalize the private salt and iron industries in 117 BCE?
• What are the earliest records of papermaking, and what is the significance of this invention?
• What was the role of Daoist religious societies in rebellions at the end of the Han dynasty (Yellow Turban Rebellion, Five Pecks of Rice Rebellion)?
• What do tomb paintings tell us about ancient Chinese society?
• What was the impact of the Sui dynasty’s standardization and re-unification of the coinage?
• What was the role of standardized testing in Sui dynasty and Tang dynasty China?
• Why is the Tang dynasty often regarded as a golden age of cosmopolitan culture in Chinese history?
• What was the role of slavery in imperial China? 
• How did the rise of jiedushi (regional military governments) undermine the civil-service system? What were the consequences of this?
• How did Tang dynasty China exert power over Japan and Korea?
• What was the Three Departments and Six Ministries system in imperial China and how did it work?
• What does the appearance of Inca, Maya, and Aztec goods in North America (Utah, Canada) and the appearance of goods from the Great Lakes region in Maya and Aztec ruins tell us about trade in the Pre-Columbian Americas?
• How did celebration of maize play a central role in Mesoamerican cultures?
• How did the Aztec empire use relationships with client city-states to establish power? How did the Aztec empire use taxation to exert power?
• How did the luxury good trade impact Aztec political power? 
• How did the building of roads play a key role in the Aztec empire?
• How and why has archaeology played a pivotal role in expanding our understanding of the pre-Columbian Americas?
• What are some common misconceptions about the Americas in the year 1491? Why do these misconceptions exist?

Latin American History (post-1492)

• How and why did the Spanish appropriate Aztec sites of significance (e.g. Mexico City at the site of Tenochtitlan)?
• What were reactions among Latin American intellectuals (e.g. Luis María Drago, Alejandro Álvarez and Baltasar Brum) to the Monroe Doctrine?
• How was the US’s involvement in the Venezuela Crisis of 1902–1903 a pivotal turning point in the relationship between the US and Latin American countries?
• What were the effects of the US’s involvement in the Cuban War for Independence?
• How did the Roosevelt Corollary of 1904 benefit the US?
• How did Simon Bolivar’s time in Europe affect his ideas about Latin American independence?
• How did 19th century academic societies play a role in the advancement of scientific discoveries? Who was excluded from these societies?
• How was music connected to the sciences in medieval thinking?
• When was the concept of zero first used, and how was it instrumental for advancements in math?
• What role did Islamic Spain play in the spread of scientific advancements in medieval Europe?
• What role has translation between languages played in the development of sciences?
• Why were Galileo’s ideas about astronomy controversial at the time?
• What was the connection between art and advancements in human anatomy?
• Why were Darwin’s ideas about natural selection controversial at the time?
• To what degree does the film Master and Commander accurately depict the voyages of Charles Darwin?
• How did the discovery of quinine and other medical innovations help to facilitate the European colonization of Africa?
• How and why was the internet invented?
• Does Virgil’s Aeneid celebrate the new Roman Empire or subvert it?
• Why was the poet Ovid exiled from Rome?
• What are the pagan influences in Beowulf ? What are the Christian elements in Beowulf ? What does that tell us about late Anglo-Saxon England?
• How does Chaucer’s Canterbury Tales reflect gender roles in late medieval England?
• How does Dante’s Inferno draw on book IV of Virgil’s Aeneid ? 
• How are gender roles presented and subverted in Shakespeare’s plays?
• To what degree did Henry David Thoreau live out the ideals he described in Walden in his own life?
• How did the serialized publication of novels affect the way that they were written?
• Does Dickens’ novel A Tale of Two Cities accurately portray the French Revolution?
• How did 18th-century novels propagate the idea of marrying for love?
• What did contemporary readers think about Jane Austen and her novels?
• To what degree do Jane Austen’s novels reflect economic realities for women in Regency England? What do they leave out?
• How did Lord Byron’s personal life affect his poetry?
• What do we know about the romantic life of Emily Dickinson?
• What were the religious movements that influenced the writer George Eliot, and how do those influences appear in her novels?
• In what ways were Walt Whitman’s writings new or different?
• How did British poets react to the horrors of Word War I?
• What do Tolkien’s letters reveal about the ways in which the two world wars influenced his writings?
• How did the friendship between CS Lewis and Tolkien affect their respective writings?
• What are the arguments for and against Catalonian independence from Spain?
• What are the arguments for and against Scottish independence from the United Kingdom?
• What are some risks of contact sports, especially for children?
• What are the most effective policies for combating childhood obesity?
• What are the most effective policies for reducing gun violence?
• Which countries have the longest life expectancy and why?
• What are some differences between the healthcare system in the US and in European countries? Which country has the most similar system to the US?
• What policies for parental leave exist in different countries? What are some effects of these policies?
• Has the drinking age in the US always been 21? What have been some different policies, and what were some consequences of them?
• What is the debate around museum artifacts like the Elgin Marbles in London or the Benin Bronzes in Berlin?
• How have politicians attempted to control population growth in different countries, either directly or indirectly? What have been some effects of these policies?
• Which countries have the most gender parity reflected in national governments? How have they accomplished this?
• How has public funding of K-12 education changed since the 1930s in the US? 
• How has public funding of higher education changed in the US?
• What is early childhood education like in different countries?
• What are some effects of free or reduced-cost meals in schools?
• How does access to menstrual products affect education outcomes for girls in different countries?
• What was the impact of Rousseau’s writings on education?
• How did Plato’s ideal forms of government reflect contemporary Athenian concerns about the unruly masses ( demos )?
• How did Aristotle justify slavery?
• How has wealth inequality increased in recent decades?
• How is inflation calculated, and what are the implications of this methodology?
• How have genetically-engineered crops changed the way that the planet feeds itself?
• How has animal testing changed since 2000?
• How is animal testing regulated differently in different countries?

Health Sciences and Psychology

• How do different societies reflect the natural circadian rhythms of the human body?
• How does secondhand smoke affect the human body?
• How does lack of sleep affect the body?
• How does stress affect the body?
• What are some ways to reduce stress?
• How have cancer treatments changed in the past 30 years?
• Why is it hard to find a “cure” for cancer?
• How has the Human Genome Project changed medical science?
• How were the Covid vaccines developed so quickly? What is the difference between the various Covid vaccines that have been developed?

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Emily graduated  summa cum laude  from Princeton University and holds an MA from the University of Notre Dame. She was a National Merit Scholar and has won numerous academic prizes and fellowships. A veteran of the publishing industry, she has helped professors at Harvard, Yale, and Princeton revise their books and articles. Over the last decade, Emily has successfully mentored hundreds of students in all aspects of the college admissions process, including the SAT, ACT, and college application essay. 

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Term Paper on Database System | Software Programs | Computer Science

Here is a term paper on ‘Database System’ for class 11 and 12. Find paragraphs, long and short term papers on ‘Database System’ especially written for college and IT students.

Term Paper on Database System

Term Paper Contents:

• Term Paper on Processing Mode

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Term Paper # 1. Introduction to Database System :

A data base system is a collection of documents, procedures, programs, manuals, etc., which help in the efficient and effective operation of data processing with a data base in use.

The most important characteristics of a data base system are:

1. Controlled Redundancy: no unnecessary duplicate fields or data items exist.

2. Complete Data Independence, or as much as possible.

3. Quick response to requests for information.

4. Ease in building up new information generating applications.

5. If required, real-time accessibility.

6. Proper security protection.

7. Maintenance of data integrity.

The data base management system has to be a dynamic one, easy to absorb new technologies which are being made available increasingly. In data base systems, like any other computer systems, there are always two methods for showing the same thing — one is called the Logical View and the other, Physical View.

In case of a database, the physical view represents how the different data are actually stored in a computer, which is quite complex. The logical view refers to how the user views the data stored for his own use, which is quite simple, although it is the same data. In other words, the physical view and logical views are linked. But in modern database systems the linkage between the logical and physical data is made transparent, the user knows nothing about how the data is actually stored and linked with other data items.

If that is not done, the cost of maintaining the current application programs would be much more than the cost to develop new ones. Hence, the concept of data base is shifting from mere independent file system to a common data base with data independence – the user being concerned with only the logical view, where data can be added, changed, deleted without having any adverse effect on the physical storage.

Term Paper # 2. The Database Structure :

Apart from storage of different group of data items under different files, also called tables, a data base system has to define the structure which links these files to each other. In fact, this relationship is what distinguishes a data base system from a system having independent, unconnected multiple data files, each going in its own way.

The data files generally contain data grouped on some common consideration, for example, there would be a data file containing the details of all employees relating to their names, address, family members, etc., and another file containing their salary details, promotion dates, etc. The reason for maintaining data files in this manner is to facilitate flexibility in use. For example, if the books on law, economics, engineering, and companies act required in a company, are all bound together, not only it would be a very fat book but it will prevent different persons to look for different matters simultaneously from the same bound volume.

With the same reasoning different data files or tables are created. But, under a database system, these files are linked with each other, so that one can get all the necessary information at one time, even though the required data may be stored in different files. This linking of data files can be done in three different types called Hierarchical, Network, and Relational.

(i) Hierarchical Structure :

In this type of structure, the records or aggregates of data items are logically conceived to be stored at different levels of hierarchy, like a tree with many branches, but being up side down. The relation between entities is established in such a manner that a data item is linked with only one data item at the next higher level, though the reverse need not be true.

For example, a Department at Level 1 of hierarchy can have many employees at Level 2, but none of the employee can be in more than one department. It can be compared to a tree structure of a family using only the male or female member. A father can have many sons, but each son has one father at the next higher level.

(ii) Network Structure :

In this type of structure, multiple relations between data items are allowed. It would be somewhat equivalent to a family diagram with both parents incorporated; so a child has a father from one family branch and a mother from another family branch. In this setup an entity may be linked up to any number of other types of entities.

In a primary school, students usually have one teacher taking all classes, where as in a college there are a number of teachers for different subjects for the same group of students, with which the students have to interact. The former is hierarchical structure with the latter being of network type.

(iii) Relational Structure :

It is quite similar to the network structure but in which the relations are executed in a specific manner, making it much simpler to conceive and execute. Different data files are linked by having a common type of data item, that is, a common data item exist in each data files. For example, each student is given a unique roll number.

Then two data base tables are built up in one of which the performance of the students are kept in different fields and in the other, details about their addresses, guardians, etc., are kept — it also having a field containing the roll number. So the field of roll number is used to link up the two data tables for the necessary data in different combinations; taking some from one-table [file] and some from the other.

In this type of structure, the data is logically conceived to be represented in a tabular form. The rows of the tables are called records by ‘commoners’ and called tuple by enthusiastics of normalization. A tuple can be said to be the values of data items, sometimes called data elements, of any entity.

The term path is used to describe a tuple representing two values. In other words, if a data file has n records, it represents n values of the entity and is called N-tuple. Incidentally, the table is called a relation, and the columns are called domains. With n fields, it is called N-ary domain.

Advantages of Relational Database :

1. Extremely simple concept, easy to use.

2. Generally assists in achieving data independence.

3. Quite flexible in operation, can be easily extended.

4. Security of important data items can be ensured by isolating them as an entity having separate access control rights.

5. Structured Query Language can be employed.

Term Paper # 3. Database Query and Scheme :

With increasing use of relational data base, a need has been felt to allow users, with little or no knowledge of programming as such, to use the data base system even in on-line mode to get the desired information generated. A new language system has accordingly come up, generally called SQL or Structured Query Language — used in dBASE IV, Oracle, etc.

Now, the involvement of a data base system is not merely limited to the storage of data; its complexity arises from the fact that it has to define the inter-relationships between various data items for efficient functioning of the data base and this has both physical and logical aspects.

When the two aspects have been insulated from each other, a general programmer operating at the high level has to know the details of the logical view of the data base — what entities are available with which attributes describing them and how these are related to each other, and this is called schema — a logical view of the data base. When a part of the logical view is considered, it is called sub-schema.

A number of matters relating to the data base system are described in the schema, some of which are:

1. Details of each of the data item or field, giving its name, type, field width, valid ranges, etc.

2. Details of formation of records from data items, and files from records; each representing a different class of entities.

3. The relationship models linking different entities whether hierar­chical, network, or relational.

4. Security aspects of the data base — password levels, etc., for different operations or user classes.

To create a data base, the statements which describe it, defining the fields etc., are written into a Data Definition File. This data definition file is inputted to a Database Description Processor, which generates an output describing the data base in terms which the Data Base Management System [DBMS] can understand — it is like compiling a source code created in a high level language.

The Database Definition produced by the Database Description Processor is under­standable by the Data Base Management System, whereas its input from Data Definition File is understandable by us.

The process is as shown below:

Term Paper # 6. Data Input :

Before feeding into the machine, the input data need to be captured or recorded in a machine usable and readable form for processing by computer. For example, the computers in 1960s mostly used 80 or 96 column paper cards as input or source documents, called Punch Cards.

In such cases recording of input data involved punching rectangular holes in the cards using alphanumeric codes representing characters. In modern computers, the recording of input data is mostly through the keyboard entries; though other input devices are also in use.

Technically, recording means transfer of data into some computer readable forms or documents. Obviously, before inputting data, these must be collected from various internal and external sources, as the case may be. Collection means gathering relevant and necessary data for generating the particular information from a mass of data which are available in abundance in any business organisation or elsewhere.

For example, in an education institution, for processing examination results of students, the marks given by different examiners and nothing else need to be collected to prepare the basic data, which is entered into the computer by using the keyboard — all other type of data, like fees paid are irrelevant.

Term Paper # 7. Data Manipulation :

The basic data processing operation carried out on the input data to add meaning to it are generally, classifying, sorting, calculating, collating, merging, searching, and summarizing.

(i) Classifying :

It is the process of organizing data into groups of similar items — into small homogeneous groups based on some specific criterion. In a co-education class, the students’ data may be classified into, male and female students for analyzing whether there is a difference in performance on account of sex.

(ii) Sorting :

It is the process of arranging data in some predetermined logical order. For example, the name of the students may be arranged on alphabetical order from a to z, or the names arranged on the basis of total marks obtained by each in some examination, in descending order, starting with the highest marks.

The criterion used for sorting, like the marks in the second case, is called key for indexing. The sorting can be done in either ascending or descending order. Another method of arranging data in a sorted manner is called indexing, which is used for data files.

(iii) Calculating :

It is the process of carrying out arithmetic computation on numerical data from the simplest addition to the complex ones — although the computer basically carries out addition in various forms. This is the most common processing job carried out at the Arithmetic & Logic Unit (ALU); where logical computation involving Boolean Algebra is also carried out.

(iv) Collating :

It is the process of comparing different set of data and then carrying out some operation on the basis of the result of comparison. It is useful in the process of merging.

(v) Merging :

It is the process of creating a third set of data by combining two different sets of data having a common field, sorted in the same logical sequence on some criterion and then combining these two sets after collating.

(vi) Searching :

It is the process of locating a particular data item from a set of data items. It is required to confirm absence or existence of a particular value. The search operation fails if the item is not found. A number of searching algorithms are available, binary search being the most popular one.

(vii) Summarizing :

It is the process of creating a few concise data items out of a mass of data. For example, the average marks computed is a summarization of the individual marks of students in a particular examination.

Term Paper # 8. Data Output :

The activities coming under output operation are displaying, storing, retrieving, and communicating.

(i) Displaying :

It is the process of showing the outcome of a processing operation on the video screen, where as, printing is doing the same thing by typing out on paper using a printer — called Hard copy — the display is called Soft Copy.

(ii) Storing :

It is the process of keeping data in a physical storage medium like tapes or disks for future use. The data is transferred from primary to secondary storage.

(iii) Retrieving :

It is the reverse process of storing. This involves getting a data or a particular set of data from a mass of data stored on a physical medium. It does not destroy the data stored.

(iv) Communicating :

It is transferring data from one source to another. It may involve different geographical regions where networks are used for transferring data. Displaying and printing are also part of the process of communi­cating.

Term Paper # 9. Data Organisation :

It is rare that some data are entered into the computer system, processed to generate information and then thrown away, like we do when using electronic calculators. In practice, there is a definite need to store data in a systematic manner for future use and this has been the area which has received most aggressive attention of computer experts. In the process, new systems have come into existence, new terminologies coined; some time referring to the same idea.

Generally the terms in common use, relating to data base systems, have two origins — one is IBM’s Data Language I [DL/I] and the other is CODASYL’s [Conference On Data System Language] Data Description Language.

Although bits or bytes are the smallest unit of recording data physically, as per IBM’s DL/I, a Field is the smallest named unit of data, a segment is a named fixed-format quantum of data containing one or more fields which interfaces between the application program and the DL/I. A Logical Data Base Record consists of named hierarchy (tree) of related segments. A Logical Data Base consists of a named collection of logical data base records. It may contain one or more type of records. [The term “named” implies that a field, segment, or a record has a definite name given to it].

Diagrammatically it is like:

Expressed in simpler language [and ignoring the part of segments, as we are dealing with data bases in general, not restricting to any specific data description language], a Field is one of the smallest units of data within each record, containing specific bits of information relating to that record and having a distinct name. A number of fields built up a Record.

For example, in our personal address books we keep data/information of our friends and acquaintances. What do we note down? The name (first-name, middle-name, last-name), house number with street/road, city/town, state, pin code, telephone number, if any, etc. Now, as per data base terminology under discussion, the detail about each person will be recorded in different fields like one field for name, another field for house number with street, etc.

In fact it is our choice how we will treat the name and address in terms of fields. For example, we can break the name and use three fields for first-name, middle-name, and last-name — it depends on what we are going to do with our data. In the address book, details of each of our friends and acquaintances will have to be filled up in the relevant fields.

How do we accommodate them? We have one record for each friend and acquaintances. We keep all the records in one address book to readily access it — similarly with computers, we store all the records together in a data file with a distinct name to it — we may call it Address Data File. So, a record contains all the data about a single item, like that for our friends and acquaintances, in the data base file.

All fields in a data file are identical in form, with each field of each record containing different data. For example, the Field 1 of Record 1 may contain Ashok, that of Record 2 may contain Kapil, and so on.

A sample of a data base file is shown below, with a few items:

If you look at the logical view of the file [the way the user looks at the data] you will notice that it is a simple two-dimensional table, with fields as the columns and the records as rows. Such a two-dimensional table is sometimes called a flat file. The table of this type is also referred to as a relation.

A set of data items can be grouped in different ways to form different records for different purposes. A group of data item within a record is referred to as a data aggregate in some system, or segments [as called by IBM] in some other. Each data item / field has to have a distinct name.

Under the CODASYL terminology, a data item is the smallest named unit of data, which is called field in IBM’s terminology; both meaning the same thing. Under this system a record is a named collection of data items, a set is a collection of records forming a two-level hierarchy, and a data base consists of a named collection of records and the set relationship between them. [CODASYL used a network approach for storing data, where as IBM’s Information Management System used hierarchical approach.]

Whatever it is, whether we denote the smallest unit of data as a field or a data item, the basic objective is to provide a systematic storage of data, which can be easily stored, quickly retrieved and easily processed to generate the desired information. The first step involved to meet this objective is to classify data objects of similar types called entities.

For example, the students of a class can be termed as entities, because they are studying together in a particular class in a particular college. Similarly, the teachers of the institution could be called another type of entities.

Under the concept of entities, we need attributes to define the entities. For example, with students as entities, each student can be uniquely identified by certain attributes — which describe each of them. The name, roll number, address, age, etc., can be used as attributes to describe a student — the distinct set of attributes defining the entity called student.

Entities can be grouped together to form a common unit for storing, say, as Student File; in which each entity has a separate but identical data structure called records. The records, as we have already seen, has a number of components called fields for each attribute, which is also called data item. Thus, there could be a field for roll number, a field for name, and so on, one for each attribute.

The fields contain data elements which uniquely identify each entity — the field name ROLLNO denote a data item, in which roll number 46 as a data element would identify say Ashok Ray. [In some literature data item and data element are used synonymously]. To summarize, broadly speaking, fields constitute a record, records constitute a data file, and data files constitute a data base.

Term Paper # 10. Data Files :

Data base files can be broadly classified into two categories depending on the permanency of data stored in relation to time — called Master File and Transaction File. The Master File is a file of almost permanent nature which contain all the data required for a given application.

On the other hand, Transaction Files are created periodically to hold data relating to current transactions like sales, purchase etc., during, say, January 1993; when Master File for sales may contain the same details about year-to-date sales. Hence the Master Files need to be periodically updated with the data from the relevant transaction files.

The process of transferring current data of relevant records to build up cumulative total in the respective fields of the master file, or adding /deleting records in the master file based on the current data of the relevant transaction file is called Updating.

As far as storage mediums are concerned, magnetic tapes have some basic limitations during updating, because even a single unit of data called block cannot be accurately overwritten [called overlaying] by new or modified data; which is possible in direct access storage devices like magnetic disks, as sectors /clusters can be overwritten.

Hence the process of updating in tapes is carried out by creating a new sorted tape file with the records to be amended/ changed/ deleted/ added and then the old master file and the new file containing amendments are run concurrently to create a new master file by merging — the new master file now containing the updated records. This new master file is naturally used during the next process of updating; creating another new master file.

Generally, the original master file is not erased (destroyed) till the second master file is created as a precautionary measure against accidental loss of data. Hence, at any instance, we have three sets of master files for any application called three generation of master files — father, son, and grandson. These type of operations are done with batch processing.

In many file operations, even with direct access storage devices, a copy of the file being handled is automatically retained along with the edited version of the file. This process is called auto-backup of file or sometimes, transaction logging and it is done as a precaution against damage to the data file inadvertently caused by software / hardware failure or mistakes — the term crash being generally used to denote destruction of files.

For example, when the Line Editor called EDLIN of MS DOS or screen editor like Wordstar, Sidekick, etc., are used to open an existing file, the original file is retained with a file extension of .bak and the new file is saved under the original filename extension, if any; the filename remaining same in both the cases. In case of a file crash, the father file with .bak extension can be used to create a new son. It is always a good practice to deliberately create backups of files as a safeguard against file crash.

In fact, MS DOS provide two utility files called BACKUP and RESTORE, specifically for this purpose. Backups can also be created in different storage media like disk files being backed up in tape cartridges; generally called check-pointed. All these measures have been developed to maintain the integrity of the data stored, be it a love letter or a financial ledger. These days, a number of utility programs are available to create backup of data and programs.

Term Paper # 11. Processing Mode :

Coming a long way from the days of mechanized accounting to EDP [Electronic Data Processing], the processing done in a modern computer can be carried out in many ways, being classified according to the time of processing in relation to the input of data — whether the output is available within seconds of inputting data or it is available after days or even weeks.

The two main techniques are classified into: 

a. Batch Processing and

b. Interactive Processing.

(i) Batch or Sequential Processing :

The input data are collected and kept in batches or groups according to the output to be generated. Then at some predetermined time, all the input data of one batch are processed together in one go. For example, after processing all the input data relating to Payroll Accounting, the stores consumption data may be processed as another batch. Normally this type of batch processing is carried out at centralized computer centres.

(ii) Interactive Processing or On-Line Processing :

Here the input data is processed the moment it is entered into the computer system, producing the necessary output, as we see in the Computerized Railway Reservation System. Obviously, it would lead to a chaotic situation if all the requests for a railway reservation are processed on weekly basis in a batch mode — this output has to be known quickly. It is called interactive, as the user is in direct communication with the computer. The term on-line indicates equipments which are in direct contact with the active computer system — which the respective terminals are.

(iii) Real Time Processing :

It is a special case of interactive [on-line] processing where the emphasis or the critical factor is the response time — the time required to process the input to generate output. It is generally adopted in cases where the computer controls other machines, where quick response is a must. For example, when computer controlled guns are fired on attacking enemy planes, the calculations to find out the firing angle has to be done quickly, so the shell hits the aircraft.

(iv) OLTP – On Line Transaction Processing :

This is also a case of interactive processing, where once the input request for a transaction, like say money transfer from one place to another, is received, it is completely processed before taking up another input. It is generally used in networks providing almost instantaneous service.

(v) In-Line or Random Processing :

Here the selected jobs are processed as per some priority scheme. Once the processing of a specific job starts, it is completely processed, generating the final output.

Related Articles:

• Term Paper on the Operating System | Software | Computer Science
• Term Paper on Database Management Package | Application Software | IT
• Term Paper on Word Processing | Application Packages | Computer Science
• Term Paper on Program Flow Chart | Programming | Computer Science

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Clean energy is boosting economic growth

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IEA (2024), Clean energy is boosting economic growth , IEA, Paris https://www.iea.org/commentaries/clean-energy-is-boosting-economic-growth, Licence: CC BY 4.0

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Clean energy is moving towards centre stage in the global energy system – and as its importance rises, a new clean energy economy is emerging .

Clean electricity accounted for around 80% of new capacity additions to the world’s electricity system in 2023, and electric vehicles for around one out of five cars sold globally. At the same time, global investment in clean energy manufacturing is booming, driven by industrial policies and market demand. Employment in clean energy jobs exceeded that of fossil fuels in 2021 and continues to grow.

Quantifying the expanding role of clean energy in the economy is therefore essential to fully understand the stakes and momentum behind energy transitions.

Clean energy accounted for 10% of global GDP growth in 2023

Our new country-by-country and sector-by-sector analysis finds that in 2023, clean energy added around USD 320 billion to the world economy. This represented 10% of global GDP growth – equivalent to more than the value added by the global aerospace industry in 2023, or to adding an economy the size of the Czech Republic to global output.

This assessment is based on a first-of-its-kind analysis of three categories of activity in the clean energy sector:

• Manufacturing of clean energy technologies : investment in clean energy manufacturing, covering the value chains for solar PV, wind power and battery manufacturing
• Deployment of clean power capacity : investment in deployment of clean electricity generation capacity – such as solar PV, wind power, nuclear power and battery storage – and in electricity networks
• Clean equipment sales : sales of electric cars (EVs) and heat pumps.

It is based on detailed project-by-project data gathered and processed by the International Energy Agency (IEA) from primary and secondary sources. We conducted this analysis at the country level, and present here the in-depth results for four of the largest economies: the United States, the European Union, China and India, which together account for two-thirds of global GDP. 1

GDP in the United States grew by a robust 2.5% in 2023. Clean energy was an important contributor: The Inflation Reduction Act and the Bipartisan Infrastructure Law drove a surge in investment in clean energy manufacturing, and sales of EVs also grew strongly. Consequently, clean energy growth accounted for around 6% of GDP growth in the world’s largest economy in 2023. This is comparable in scale to the contribution to GDP growth in 2023 from the United States’ booming, artificial-intelligence-driven digital economy. 2

Clean energy accounted for around one-fifth of China’s 5.2% GDP growth in 2023. Each of the three categories assessed grew strongly, with the largest increase coming from investment in clean power capacity, followed by clean equipment sales, particularly EVs. Expansion in clean energy manufacturing accounted for around 5% of China’s GDP growth in 2023, although the country’s surplus production capacity in technologies such as batteries (utilisation rates were around 30% in 2023) may limit the scope of this growth driver going forward. Similar assessments have come to comparable conclusions, albeit with slightly different boundaries.  

In the European Union, clean energy accounted for nearly one-third of GDP growth in 2023, the highest share of any region assessed, although its share is inflated by weak overall GDP growth of around 0.5%. Nonetheless, the EU’s strong climate targets and policies, such as the Fit for 55 package and the proposed Net Zero Industry Act, are supporting investments in clean energy manufacturing, which more than doubled between 2022 and 2023, driven in particular by battery manufacturing.

India was the fastest growing large economy in 2023, with GDP increasing by around 7.7%. Clean energy contributed slightly less than 5% of GDP growth in 2023, predominantly from investment in new solar power capacity. Meanwhile, policies such as the Production Linked Incentive are attracting investment in new clean energy manufacturing capacity. In 2023, this remained relatively small as a portion of India’s overall economy, but interest from businesses and investors is increasing.

Contribution of investment and sales in selected clean energy technologies to GDP growth, 2023

Assessing the extent to which different sectors of the clean energy economy contribute to GDP growth from year to year helps show the direction of travel. Yet looking at their share of GDP in a single year is also useful in understanding their economic importance. In 2023, clean energy investment and sales accounted for between 1% and 4% of total GDP in the four major regions assessed – substantial shares in the context of these large and diversified economies. The chemicals industry accounts for about 3% of value added in India and China. Clean energy technologies therefore already provide a sizable contribution to GDP in these economies today.

The clean energy sector also drove a substantial share of total investment growth across the economy in these regions in 2023. In the case of China, it contributed 50% of the growth in total investment in 2023, and 20% in the United States. At the global level, we estimate that around USD 200 billion was invested in clean energy technology manufacturing in 2023, an increase of 75% over the previous year. This compares with global capital investment in semiconductor manufacturing of around USD 170 billion to 250 billion per year in recent years.

Share of investment and sales in selected clean energy technologies in GDP, 2023

Share of investment in selected clean energy technologies in total investment, 2023.

This analysis highlights the scale and weight of the clean energy economy. It shows that it is not only growing quickly, but also has already become a powerful economic force. As energy transitions advance, clean energy’s importance for economies around the world is only set to grow further.

Modernising energy and industrial systems to drive energy transitions requires very large investments and the transformation of huge markets. It also comes with many significant benefits beyond mitigating climate change and reducing air pollution alone; in 2023, 36 million workers were employed across clean energy supply chains.

And while China still leads in investment in the manufacturing of clean energy technologies, other regions are also seeing a jump in projects and investments. The large share of one country has raised questions about the resilience and diversity of global clean energy technology supply chains, but it also currently provides opportunities to accelerate global decarbonisation based on an abundant supply of low-cost clean energy equipment. The analysis developed here highlights the importance of comprehensively assessing the size of the clean energy economy when designing energy, climate and industrial policies.

The estimates for investment in the manufacturing of clean energy technologies come from a first-of-its-kind analysis that builds on methodologies developed in the IEA’s The State of Clean Technology Manufacturing report and Energy Technology Perspectives reports. The detailed findings will be presented in the upcoming Energy Technology Perspectives Special Report Advancing Clean Technology Manufacturing , requested by G7 Leaders at the 2023 Hiroshima Summit in Japan. Investment in the deployment of clean power capacity comes from the forthcoming edition of our World Energy Investment series. Macroeconomic data comes from Oxford Economics, based on national sources, in order to ensure cross-country consistency. 

In 2023 , the “data processing, internet publishing, and other information services” sector contributed around 9% to real growth in gross value added (GVA).

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