phd thesis on database management system

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.

Research Centers

• EPIC Data lab
• Sky Computing Lab
• Alvin Cheung
• Natacha Crooks
• Joseph Gonzalez
• Joseph M. Hellerstein (coordinator)
• Jiantao Jiao
• Aditya Parameswaran
• Matei Zaharia
• Eric Brewer
• Michael Lustig
• Jelani Nelson

Faculty Awards

• ACM Prize in Computing: Eric Brewer, 2009.
• National Academy of Engineering (NAE) Member: Ion Stoica, 2024. Eric Brewer, 2007.
• American Academy of Arts and Sciences Member: Eric Brewer, 2018.
• Sloan Research Fellow: Aditya Parameswaran, 2020. Alvin Cheung, 2019. Jelani Nelson, 2017. Michael Lustig, 2013. Ion Stoica, 2003. Joseph M. Hellerstein, 1998. Eric Brewer, 1997.

Related Courses

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

Help | Advanced Search

Computer Science > Databases

Title: phd thesis: sql comprehension and synthesis.

Abstract: Structured Query Language (SQL) remains the standard language used in Relational Database Management Systems (RDBMSs) and has found applications in healthcare (patient registries), businesses (inventories, trend analysis), military, education, etc. Although SQL statements are English-like, the process of writing SQL queries is often problematic for nontechnical end-users in the industry. Similarly, formulating and comprehending written queries can be confusing, especially for undergraduate students. One of the pivotal reasons given for these difficulties lies with the simple syntax of SQL, which is often misleading and hard to understand. An ideal solution is to present these two audiences: undergraduate students and nontechnical end-users with learning and practice tools. These tools are mostly electronic and can be used to aid their understanding, as well as enable them to write correct SQL queries. This work proposes a new approach aimed at understanding and writing correct SQL queries using principles from Formal Language and Automata Theory. We present algorithms based on: regular expressions for the recognition of simple query constructs, context-free grammars for the recognition of nested queries, and a jumping finite automaton for the synthesis of SQL queries from natural language descriptions. As proof of concept, these algorithms were further implemented into interactive software tools aimed at improving SQL comprehension. Evaluation of these tools showed that the majority of participants agreed that the tools were intuitive and aided their understanding of SQL queries. These tools should, therefore, find applications in aiding SQL comprehension at higher learning institutions and assist in the writing of correct queries in data-centered industries.

Submission history

Access paper:.

• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Your browser is unsupported

We recommend using the latest version of IE11, Edge, Chrome, Firefox or Safari.

PhD in Management Information Systems

Research in Information Systems and Technological Progress

The UIC Business PhD program in Management Information Systems educates and trains future scholars to establish successful careers as productive researchers, scientists and scholars at leading universities and research institutions. Our program stresses technical, economic and organizational/management aspects of information systems.

A STEM Degree Program

The PhD in Management Information Systems is approved as a STEM (Science, Technology, Engineering and Mathematics) Designated Degree Program. Under the Optional Practical Training program, international students who graduate from the program are able to remain in the United States and receive training through work experience for up to 12 months, and can remain for an additional 24 months on an OPT STEM extension.

Learn More Heading link Copy link

• Edit icon Request Information
• Check icon Apply Now

Meet the Team Heading link Copy link

• Current PhD Students Learn more about the program and see whether you would be a good fit here at UIC by reading about our current students’ backgrounds and research interests.
• Program Faculty Read about faculty areas of research and teaching focus.

Important Dates Heading link Copy link

The application deadline for this program is January 15.

Admission Heading link Copy link

Admission is competitive and applicants are considered on an individual basis. The college considers applications for full time degree seeking status for the Fall term only. The deadline to submit the application, fee and required materials is January 15. Please see the admissions section of our catalog for application requirements

Requirements and Dissertation Heading link Copy link

Requirements.

Breadth Requirement

Two introductory courses in any two functional areas of business, for example:

• Operations management (IDS 532)
• Accounting (ACTG 500)
• Economics (ECON 520,521)
• Finance (FIN 500)
• Marketing (MKTG 500)
• Management (MGMT 541)

These courses will not count towards the 64 semester hours required for entrants with a master’s degree.

Technical Requirements

• Data Structures and Operating Systems (IDS 401)
• Business Systems Analysis & Design (IDS 405)
• Business Database Design (IDS 410)

Each course may be waived based on equivalent prior coursework or appropriate work experience in the technical area. These courses will not count towards the 64 semester hours requirement for entrants with a master’s degree.

Basic Competency

• Advanced database management (IDS 520)
• Distributed processing and telecommunication systems (IDS 521)
• Enterprise application infrastructure (IDS 517)

Each course may be waived based on equivalent prior coursework or appropriate work experience in the technical area.

Research Methods: 3-4 courses (12-16 semester hours) including statistical methods in research, behavioral research methods overview, quantitative methods in research and additional courses to be decided in consultation with the director of the PhD program.

MIS Specialization: Minimum of six courses (24 credit hours) including two IDS research seminars (IDS 529), three specialized courses in areas of individual interest, IS research topics (IDS 525), and an additional courses in consultation with the director of the PhD program.

Additional doctoral-level course work, including dissertation: at least 8 courses (32 semester hours minimum.

IDS 599: PhD Thesis Research or additional doctoral-level course work chosen with the consent of the PhD coordinator and in consultation with the dissertation adviser. A maximum of 32 semester hours of thesis research can count toward the degree.

Annual Evaluation The student will write a research paper each year beginning the first summer in the program and continuing until the preliminary evaluation. An assessment of the summer paper will be conducted at the beginning of the fall semester each year that the student is in the program, except the first year. The evaluation will be conducted by a three-member faculty committee, which will include the PhD director as the chairperson, the student’s mentor and an MIS faculty member who has taught the student during that year.

Preliminary Examination The preliminary examination is normally taken upon successful completion of the required course work. In exceptional cases, the examination may be taken earlier upon recommendation of the MIS director of doctoral studies and the student’s PhD adviser. The Graduate College requires that “the preliminary examination may not be given before one calendar year of residence nor later than one calendar year before defense of the dissertation.”

The preliminary examination consists of an oral examination on the dissertation proposal and related material. The examination may cover any issues relevant to the topic addressed in the proposal and PhD common core and basic knowledge in the field of specialization related to the proposal.

For a full list of degree requirements, click here for the UIC Catalog .

Dissertation

A dissertation, which makes an original contribution to knowledge in MIS, is required and must be defended. Dissertations may address theoretical or applied problems. In most cases, a minimum of 24 semester hours will be required to prepare a dissertation acceptable to the committee. Up to 32 semester hours of credit can be awarded for successful completion of a dissertation.

Explore the UIC Catalog

Placements heading link copy link, 2023 placements.

• Tengteng Ma, University of South Florida
• Moh Hosseinioun, Postdoc, Northwestern University

2022 Placements

• Ramah Al Balawi, Baruch College at CUNY

2021 Placements

• Keran Zhao, T. Bauer College of Business, University of Houston
• Atiya Avery, Auburn University
• Ecem Basak, Baruch College, City University of New York

2020 Placements

• Pankhuri Malhotra, Michael F. Price College of Business, The University of Oklahoma
• Cheng Chen, Lubar School of Business, University of Wisconsin-Milwaukee

2018 Placements

• Pouya Rahmati, Deloitte, University of Georgia (Prior)
• Minghong Xu, Practice at Carey Business School, Johns Hopkins
• Atiya Avery, University of Alabama in Huntsville
• Amer Aljarallah, King Saud University
• Sridhar R. Papagari Sangareddy, Centers for Disease Control
• Mohan Thirumalai, University of Alabama at Birmingham
• Ivan Alfaro, Wellspring
• Ferdi Eruysal, Texas A&M
• Melike Findikogu, Technion – Israel Institute of Technology
• Doug Lundquist, UIC
• Mauricio Vasquez, University of Puerto Rico
• Poornima Krishnan, Instructor, North Central College / Research Analyst, Sullivan, Cotter & Associates
• Sanjeev Jha, University of New Hampshire
• Ariel Lapaz, Departamento de Control de Gestion y Sistemas de Informacion, Chile
• Chen Ye, Virginia State University
• Christina Outlay, DePaul University
• Darrin Thomas, Morningstar Inc.
• Dong Back Sen, University of Groningen, Netherlands
• Kirill Yurov, Northeastern Illinois University
• Kurian Tharakkunel, Rowan University, NJ
• Chei Sian Lee, Nanyang Technological University, Singapore
• Kevin Desouza, University of Washington
• Yifeng Zhang, University of Illinois at Springfield
• John Warren, University of Texas at San Antonio
• James Watson, CEO, Doculabs, Chicago
• Kumar Mehta, University of Connecticut

Open Access Theses and Dissertations

Thursday, April 18, 8:20am (EDT): Searching is temporarily offline. We apologize for the inconvenience and are working to bring searching back up as quickly as possible.

Advanced research and scholarship. Theses and dissertations, free to find, free to use.

Advanced search options

Browse by author name (“Author name starts with…”).

Find ETDs with:

Written in any language English Portuguese French German Spanish Swedish Lithuanian Dutch Italian Chinese Finnish Greek Published in any country US or Canada Argentina Australia Austria Belgium Bolivia Brazil Canada Chile China Colombia Czech Republic Denmark Estonia Finland France Germany Greece Hong Kong Hungary Iceland India Indonesia Ireland Italy Japan Latvia Lithuania Malaysia Mexico Netherlands New Zealand Norway Peru Portugal Russia Singapore South Africa South Korea Spain Sweden Switzerland Taiwan Thailand UK US Earliest date Latest date

Sorted by Relevance Author University Date

Only ETDs with Creative Commons licenses

Results per page: 30 60 100

October 3, 2022. OATD is dealing with a number of misbehaved crawlers and robots, and is currently taking some steps to minimize their impact on the system. This may require you to click through some security screen. Our apologies for any inconvenience.

Recent Additions

See all of this week’s new additions.

About OATD.org

OATD.org aims to be the best possible resource for finding open access graduate theses and dissertations published around the world. Metadata (information about the theses) comes from over 1100 colleges, universities, and research institutions . OATD currently indexes 7,241,108 theses and dissertations.

About OATD (our FAQ) .

Visual OATD.org

We’re happy to present several data visualizations to give an overall sense of the OATD.org collection by county of publication, language, and field of study.

You may also want to consult these sites to search for other theses:

• Google Scholar
• NDLTD , the Networked Digital Library of Theses and Dissertations. NDLTD provides information and a search engine for electronic theses and dissertations (ETDs), whether they are open access or not.
• Proquest Theses and Dissertations (PQDT), a database of dissertations and theses, whether they were published electronically or in print, and mostly available for purchase. Access to PQDT may be limited; consult your local library for access information.

Librarians/Admins

• EBSCOhost Collection Manager
• EBSCO Experience Manager
• EBSCO Connect
• Start your research
• EBSCO Mobile App

Clinical Decisions Users

• DynaMed Decisions
• Dynamic Health
• Waiting Rooms
• NoveList Blog

EBSCO Open Dissertations

EBSCO Open Dissertations makes electronic theses and dissertations (ETDs) more accessible to researchers worldwide. The free portal is designed to benefit universities and their students and make ETDs more discoverable. 

Increasing Discovery & Usage of ETD Research

EBSCO Open Dissertations is a collaboration between EBSCO and BiblioLabs to increase traffic and discoverability of ETD research. You can join the movement and add your theses and dissertations to the database, making them freely available to researchers everywhere while increasing traffic to your institutional repository. 

EBSCO Open Dissertations extends the work started in 2014, when EBSCO and the H.W. Wilson Foundation created American Doctoral Dissertations which contained indexing from the H.W. Wilson print publication, Doctoral Dissertations Accepted by American Universities, 1933-1955. In 2015, the H.W. Wilson Foundation agreed to support the expansion of the scope of the American Doctoral Dissertations database to include records for dissertations and theses from 1955 to the present.

How Does EBSCO Open Dissertations Work?

Your ETD metadata is harvested via OAI and integrated into EBSCO’s platform, where pointers send traffic to your IR.

EBSCO integrates this data into their current subscriber environments and makes the data available on the open web via opendissertations.org .

You might also be interested in:

MIT Libraries home DSpace@MIT

• DSpace@MIT Home
• MIT Libraries

Doctoral Theses

Theses by department.

• Computational and Systems Biology
• Department of Aeronautics and Astronautics
• Department of Architecture
• Department of Biological Engineering
• Department of Biology
• Department of Brain and Cognitive Sciences
• Department of Chemical Engineering
• Department of Chemistry
• Department of Civil and Environmental Engineering
• Department of Earth, Atmospheric, and Planetary Sciences
• Department of Economics
• Department of Electrical Engineering and Computer Sciences
• Department of Humanities
• Department of Linguistics and Philosophy
• Department of Materials Science and Engineering
• Department of Mathematics
• Department of Mechanical Engineering
• Department of Nuclear Science and Engineering
• Department of Ocean Engineering
• Department of Physics
• Department of Political Science
• Department of Urban Studies and Planning
• Engineering Systems Division
• Harvard-MIT Program of Health Sciences and Technology
• Institute for Data, Systems, and Society
• Media Arts & Sciences
• Operations Research Center
• Science, Technology & Society
• Sloan School of Management
• Technology and Policy Program

Recent Submissions

Electron beam techniques for testing and restructuring of wafer-scale integrated circuits 

On the synthesis of metastable A-15 "Nb3Si" by ion implantation. 

Designing Macromolecules using Machine Learning and Simulations 

DIMA course offerings for the Summer Semester 2024

Berlin learning and intelligent systems society: speaker series, dima researchers at the edbt 2024, two research papers accepted at sigmod 2024, from distributed data to large ai models.

Harvard University Theses, Dissertations, and Prize Papers

The Harvard University Archives ’ collection of theses, dissertations, and prize papers document the wide range of academic research undertaken by Harvard students over the course of the University’s history.

Beyond their value as pieces of original research, these collections document the history of American higher education, chronicling both the growth of Harvard as a major research institution as well as the development of numerous academic fields. They are also an important source of biographical information, offering insight into the academic careers of the authors.

Spanning from the ‘theses and quaestiones’ of the 17th and 18th centuries to the current yearly output of student research, they include both the first Harvard Ph.D. dissertation (by William Byerly, Ph.D . 1873) and the dissertation of the first woman to earn a doctorate from Harvard ( Lorna Myrtle Hodgkinson , Ed.D. 1922).

Other highlights include:

• The collection of Mathematical theses, 1782-1839
• The 1895 Ph.D. dissertation of W.E.B. Du Bois, The suppression of the African slave trade in the United States, 1638-1871
• Ph.D. dissertations of astronomer Cecilia Payne-Gaposchkin (Ph.D. 1925) and physicist John Hasbrouck Van Vleck (Ph.D. 1922)
• Undergraduate honors theses of novelist John Updike (A.B. 1954), filmmaker Terrence Malick (A.B. 1966),  and U.S. poet laureate Tracy Smith (A.B. 1994)
• Undergraduate prize papers and dissertations of philosophers Ralph Waldo Emerson (A.B. 1821), George Santayana (Ph.D. 1889), and W.V. Quine (Ph.D. 1932)
• Undergraduate honors theses of U.S. President John F. Kennedy (A.B. 1940) and Chief Justice John Roberts (A.B. 1976)

What does a prize-winning thesis look like?

If you're a Harvard undergraduate writing your own thesis, it can be helpful to review recent prize-winning theses. The Harvard University Archives has made available for digital lending all of the Thomas Hoopes Prize winners from the 2019-2021 academic years.

Accessing These Materials

How to access materials at the Harvard University Archives

How to find and request dissertations, in person or virtually

How to find and request undergraduate honors theses

How to find and request Thomas Temple Hoopes Prize papers

How to find and request Bowdoin Prize papers

• email: Email
• Phone number 617-495-2461

Related Collections

Harvard faculty personal and professional archives, harvard student life collections: arts, sports, politics and social life, access materials at the harvard university archives.

For enquiries call:

+1-469-442-0620

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.

Monica Gupta

I am Monica Gupta with 19+ years of experience in the field of Training and Development. I have done over 500 Corporate Trainings. I am currently working as a freelancer for several years. My core area of work is Java, C++, Angular, PHP, Python, VBA.

Avail your free 1:1 mentorship session.

Something went wrong

Upcoming Database Batches & Dates

Responsible Thesis-Writing Process

• Information searching

Research data management and the FAIR principles

Stages of data management, data protection (gdpr).

• Interview and Survey Data
• Research ethics
• Research notification
• Research permission
• Business collaboration
• Accessibility
• Publishing thesis
• Save in Osuva
• More useful information

Research data refers to any data with which the analysis and results of a study can be repeated and validated. The data may have been collected by the researcher, generated during the study or consist of pre-existing archival data, and include various measurement results, survey and interview data, notes, research diaries, software or source codes.

Data management refers to the systematic collection, processing, storing and description of research data. Students are encouraged to learn about data management early in their studies, because good data management skills are beneficial to study progress and to adopting suitable data management practices during the thesis-writing process.

Data management practices should seek to comply with the FAIR principles, ensuring that the data is

• Accessible,
• Interoperable and

This is achieved, for example, through the use of open file formats, comprehensive metadata, and persistent identifiers (e.g., DOI, URN, ORCID), and defining ownership, terms of use and licenses. Learn more about the FAIR principles and the policy component for open access to research data .

1. Planning

Before you collect any data, record the most suitable data practices in a Data Management Plan (DMP) that can be supplemented as the work progresses and plans become more accurate. Formulating a plan will help you identify potential data protection risks, as well as solutions suitable for storing and describing your data. Careful data management also allows you to make the data accessible for potential reuse and thus improve the reliability of your research and the repeatability of the results.

Planning can be done using the DMPTuuli tool that is accessible with your HAKA credentials. DMPTuuli contains templates and instructions that can be applied to the data management plan of a thesis.

2. Storing and Organising Data

Choose a secure storage solution for your data, based on the demand for the data and its confidentiality level. Secure storage, version control and backup help prevent any unintentional deletion of data. Open file formats, logical file naming and folder structure, as well as rich content descriptions facilitate the findability, intelligibility and sharing of data. Consider the following questions when choosing the storage solution:

• Ensure secure access control so that only authorised parties have access to the data.
• Utilise the university’s cloud storage solutions (e.g., Owncloud, SharePoint) suitable for data sharing.
• Protect confidential data with a password or encryption.
• Confidential data should not be stored in commercial cloud services, such as Dropbox or Google Drive.
• If you store data on your personal devices, ensure backups, device password protection and anti-virus protection.
• If you use services provided by the university, using automatic backup solutions is recommended.

NB: External storage media, such as flash drives, are not recommended as primary storage solutions, because data stored on them is susceptible to becoming lost, deleted and unintentionally shared with outsiders.

Backing up helps you decrease the risk of irreparable damage to or deletion of data. Always keep separate working and backup copies of the research data. Choose storage solutions that include automatic backup. Backing up should be based on the 3-2-1 Rule, meaning that data is stored in the following way:

• in at least three copies
• on two different types of storage media
• one of which is kept physically separate from the others.

File formats

To ensure the usability of your data on a variety of devices and software, using open, non-commercial file formats is recommended. Most software supports the following common file formats:

• Text: txt, .odt., .rtf, .csv, PDF/A, .html,.xml
• Images: jpeg, tiff, png, dng
• Video: MPEG-4 (.mp4), dpx
• Sound:  FLAC, aif, aac.

Naming conventions and Folder structure

Systematic file naming practices and folder structures ensure the identifiability and findability of your data, even when there are time lapses in processing it. Clear file naming also simplifies file sharing. When you name a file:

• Choose a descriptive name
• Avoid names that are too long or short
• Avoid special characters and spaces
• To separate parts of the name, use the underscore (_), hyphen (-) or initial capital letters
• Add dates, version numbers and/or modifier initials to distinguish between different file versions
• Avoid overlap in folder and file names

NB: If you use abbreviations, remember to define them in writing so that they can be understood.

Documentation

Document the basics of your data during the thesis writing process to ensure the findability and usability of your data. Documenting makes it easy to check the contents of your data, how it has been processed and where it is stored. The simplest option is to record the descriptive data (or metadata) related to your data in a text file (a.k.a. README file) that you save as a separate file along with your data. Metadata may also be published according to the description guidelines of the particular publishing service. Record at least the following information in the file:

• Data name, size and file format
• Data content and descriptions of variables (abbreviations, measuring scales, coding)
• Data collection (who, where, when, how)
• Data processing (who, how, when)
• Storing data and terms and conditions

Read more about storing, file naming , recommended file formats and documenting in the Data Management Guidelines of the Finnish Social Science Data Archive.

3. Publishing, archiving or deleting

Take care of your research data even after the completion of the thesis. Electronic data requires further measures to stay up-to-date, and not all data needs to be archived for long periods of time. Based on the reuse value of your data, choose appropriate measures, such as data archiving, publishing or deletion. Keep in mind that your right to use the University of Vaasa IT services expires after graduation, unless you continue in another university role, such as a position of doctoral researcher or employee. If the data is stored in the University of Vaasa systems, remember to transfer or delete it before your access rights expire.

If your data contains personal details, it is usually deleted after the thesis has been accepted. Keep in mind that moving a file to the recycle bin does not sufficiently delete the data. More thorough measures, such as overwriting a drive or mechanically destroying a flash drive, are needed. Further information on deleting the data: Office of the Data Protection Ombudsman or Data Management Guidelines of the Finnish Social Science Data Archive .

If the data has reuse value and you have permission to reuse or publish the data, you may publish or archive your data in a chosen data archive. Keep in mind that you may need permission for data reuse or publishing from your research subjects or potential customer, and that data anonymisation may be a condition for publishing. For example, the Finnish Social Science Data Archive , The Language Bank of Finland , and Fairdata’s IDA and Qvain offer domestic solutions for publishing data and related metadata, while Zenodo or EUDAT  B2Share are some of the international service provider options.

Data protection refers to the safeguarding of personal data. The notion of personal data is broad, and what qualifies as personal data is any information that either directly or indirectly enables the identification of a person, for example by connecting an individual piece of information to another piece of information. More information on personal data: Office of the Data Protection Ombudsman . Personal data processing related to studies must adhere to the principles of the University of Vaasa data protection policy: University of Vaasa Information Security Policy .

Before collecting and processing personal data:

• Discuss the issue with your thesis supervisor or the teacher in charge of the course
• Familiarise yourself with the University of Vaasa personal data processing instructions: Data Processing Instructions (NB: The instructions can only be accessed by university personnel) and University of Vaasa Data Protection Statement (NB: currently available only in Finnish).
• Be sure to provide the research participants with a privacy notice. Further reading available in this guide under Privacy Notice .

While collecting and processing personal data:

• Limit the processing of personal data to what is necessary to achieve the aims of the thesis. Do not collect personal data “just in case”!
• Store personal data in a secure way and ensure that third parties do not accidentally or intentionally gain access to personal data.

The student collecting personal data acts as the data controller.

Useful links

• << Previous: Information searching
• Next: Interview and Survey Data >>
• Last Updated: May 17, 2024 3:01 PM
• URL: https://uva.libguides.com/responsible-thesis

• USF Research
• USF Libraries

Digital Commons @ USF > Muma College of Business > Management > Theses and Dissertations

Management and Organization Theses and Dissertations

Theses/dissertations from 2023 2023.

For Love or Money: Investor Motivations in Equity-Based Crowdfunding , Jason C. Cherubini

The Great Resignation: An Exploration of Strategies to Combat School Bus Driver Shortages in the Post-COVID-19 Era , James E. Cole Jr.

An Empirical Analysis of Sentiment and Confidence Regarding Interest Rates in Disclosures of Public Firms in the U.S. Fintech Sector , James J. Farley

Motivations for Planning: Uncovering the Inhibitors to the Adoption of Comprehensive Financial Planning for Business Owners , Daniel R. Gilham

An Examination of Reward-Based Crowdfunding Performance and Success , Matthew Alan Grace

All Quiet on The Digital Front: The Unseen Psychological Impacts on Cybersecurity First Responders , Tammie R. Hollis

Commitment to Change Dimensions: The Influence of Innovative Work Behavior and Organizational Environments , Michael Holmes

Turmoil in the Workforce: Introduction of the Nomadic Employee , Catrina Hopkins

Attention-Grabbing Tactics on Social Media , Arjun Kadian

Theses/Dissertations from 2022 2022

Building a Mentor-Mentee Maturity Model , Leroy A. Alexander

Do Auditors Respond to Changes in Clients’ Analyst Coverage? Evidence from a Natural Experiment , Mohammad Alkhamees

Designing a Messaging Strategy to Improve Information Security Policy Compliance , Federico Giovannetti

Are all pictures worth 1,000 words? An Investigation of Fit Between Graph Type and Performance on Accounting Data Analytics Tasks , Shawn Paul Granitto

An Enterprise Risk Management Framework to Design Pro-Ethical AI Solutions , Quintin P. McGrath

Deceptive Appeals and Cognitive Influences Used in Fraudulent Scheme Sales Pitches , Rafael J. Toledo

Using Online Reviews to Identify How Hotels Can Satisfy Travelers With Pets While Making Money , Sonia Weinhaus

Theses/Dissertations from 2021 2021

The IS Social Continuance Model: Using Conversational Agents to Support Co-creation , Naif Alawi

The Use of Data Analytic Visualizations to Inform the Audit Risk Assessment: The Impact of Initial Visualization Form and Documentation Focus , Rebecca N. Baaske (Becca)

Identification of Entrepreneurial Competencies in I-Corps Site Teams at the University of South Florida , Mark A. Giddarie

Understanding Nonprofit Boards: An Exploratory Study of the Governance Practices of Regional Nonprofits , Susan Ryan Goodman

Strengthening the Entrepreneurial Support Community , Andrew J. Hafer

Who to Choose? Rating Broker Best Practices in the Medicare Advantage Industry , Darwin R. Hale

Bridging the Innovatino Gap at SOCOM , Gregory J. Ingram

Improving Environmental Protection: One Imagined Touch at a Time , Luke Ingalls Liska

Residential Curbside Recycle Context Analysis , Ntchanang Mpafe

Fighting Mass Diffusion of Fake News on Social Media , Abdallah Musmar

Managing Incomplete Data in the Patient Discharge Summary to Support Correct Hospital Reimbursements , Fadi Naser Eddin

GAO Bid Protests by Small Business: Analysis of Perceived and Reported Outcomes in Federal Contracting , David M. Snyder

Engagement and Meaningfulness as Determinants of Employee Retention: A Longitudinal Case Study , Calvin Williams

Public Budgeting as Moral Dilemma , Ben Wroblewski

Theses/Dissertations from 2020 2020

Improving Engagement: The Moderating Effect of Leadership Style on the Relationship Between Psychological Capital and Employee Engagement , Scott Beatrice

Physician Self-Efficacy and Risk-Taking Attitudes as Determinants of Upcoding and Downcoding Errors: An Empirical Investigation , Samantha J. Champagnie

Digital Identity: A Human-Centered Risk Awareness Study , Toufic N. Chebib

Clarifying the Relationship of Design Thinking to the Military Decision-Making Process , Thomas S. Fisher

Essays on the Disposition Effect , Matthew Henriksson

Analysis of Malicious Behavior on Social Media Platforms Using Agent-Based Modeling , Agnieszka Anna Onuchowska

Who Rises to the Top: An Investigation of the Essential Skills Necessary for Partners of Non-Big 4 Public Accounting Firms , Amanda K. Thompson-Abbott

Theses/Dissertations from 2019 2019

The Financial and Nonfinancial Performance Measures That Drive Utility Abandonments and Transfers in the State of Florida , Daniel Acheampong

Locating a New Collegiate Entrepreneurship Program, a Framework for a University Campus , Douglas H. Carter

Understanding Employee Engagement: An Examination of Millennial Employees and Perceived Human Resource Management Practices , Danielle J. Clark

The Potential Impact Radius of a Natural Gas Transmission Line and Real Estate Valuations: A Behavioral Analysis , Charles M. Hilterbrand Jr.

Introducing a Mobile Health Care Platform in an Underserved Rural Population: Reducing Assimilations Gaps on Adoption and Use via Nudges , Joseph Hodges

Controlling Turnover in an Inside Sales Organization: What are the Contributing Factors , Dennis H. Kimerer

An Emergent Theory of Executive Leadership Selection: Leveraging Grounded Theory to Study the U.S. Military's Special Forces Assessment and Selection Process , Darryl J. Lavender

Essays on Migration Flows and Finance , Suin Lee

The Underutilized Tool of Project Management - Emotional Intelligence , Gerald C. Lowe

Increasing the Supply of the Missing Middle Housing Types in Walkable Urban Core Neighborhoods: Risk, Risk Reduction and Capital , Shrimatee Ojah Maharaj

Playing Darts in the Dark: How are Chamber of Commerce Leaders Aligned for Greater Effectiveness? , Robert J. Rohrlack Jr.

Are Transfer Pricing Disclosures Related to Tax Reporting Transparency? The Impact of Auditor-Provided Transfer Pricing Services , Stephanie Y. Walton

Theses/Dissertations from 2018 2018

Price Transparency in the United States Healthcare System , Gurlivleen (Minnie) Ahuja

How to Build a Climate of Quality in a Small to Medium Enterprise: An Action Research Project , Desmond M. Bishop III

Banking on Blockchain: A Grounded Theory Study of the Innovation Evaluation Process , Priya D. Dozier

Enhancing the Design of a Cybersecurity Risk Management Solution for Communities of Trust , James E. Fulford Jr.

An Examination of the Progressive and Regressive Factors that Business Owners Consider When Choosing Whether or Not to Implement an Exit Strategy , David C. Pickard

The Relationship between Ambient Lighting Color and Hotel Bar Customer Purchase Behavior and Satisfaction , Kunal Shah

The Unmanned Aerial Systems (UASs) Industry and the Business Impacts of the Evolution of the Federal Regulatory Environment , Darren W. Spencer

Intercultural Communication Between International Military Organizations; How Do You Turn a ‘No’ Into a ‘Yes’? , Douglas A. Straka

Essential Leadership Skills for Frontline Managers in a Multicultural Organization , Janelle Ward

Moffitt Cancer Center: Leadership, Culture and Transformation , W. James Wilson

Two Essays on String of Earnings Benchmarks , Yiyang Zhang

Theses/Dissertations from 2017 2017

Multi-Step Tokenization of Automated Clearing House Payment Transactions , Privin Alexander

The Effect of Corporate Social Responsibility Investment and Disclosure on Cooperation in Business Collaborations , Sukari Farrington

What Factors during the Genesis of a Startup are Causal to Survival? , Gilbert T. Gonzalez

The Great Recession of 2007 and the Housing Market Crash: Why Did So Many Builders Fail? , Mohamad Ali Hasbini

The Effect of Expanded Audit Report Disclosures on Users’ Confidence in the Audit and the Financial Statements , Peter Kipp

An Examination of Innovation Idea Selection Factors in Large Organizations , Troy A. Montgomery

Essays on Sales Coaching , Carlin A. Nguyen

Vital Signs of U.S. Osteopathic Medical Residency Programs Pivoting to Single Accreditation Standards , Timothy S. Novak

Leaders Who Learn: The Intersection of Behavioral Science, Adult Learning and Leadership , Natalya I. Sabga

Toward a Systemic Model for Governance and Strategic Management: Evaluating Stakeholder Theory Versus Shareholder Theory Approaches , James A. Stikeleather

A Longitudinal Study of the Effects of Cognitive Awareness Training on Transaction Processing Accuracy: An Introduction to the ACE Theoretical Construct , John Townsend

Theses/Dissertations from 2016 2016

The Effect of Presentation Format on Investor Judgments and Decisions: Does the Effect Differ for Varying Task Demands? , Kevin Agnew

Theses/Dissertations from 2014 2014

Multi-Task Setting Involving Simple and Complex Tasks: An Exploratory Study of Employee Motivation , Maia Jivkova Farkas

Essays on Mergers and Acquisitions , Marcin Krolikowski

Do Social Biases Impede Auditor Reliance on Specialists? Toward a Theory of Social Similarity , Rina Maxine Limor

Theses/Dissertations from 2013 2013

Psychological Distance: The Relation Between Construals, Mindsets, and Professional Skepticism , Jason Rasso

Theses/Dissertations from 2011 2011

Combining Natural Language Processing and Statistical Text Mining: A Study of Specialized Versus Common Languages , Jay Jarman

An Empirical Investigation of Decision Aids to Improve Auditor Effectiveness in Analytical Review , Robert N. Marley

The Effects of Item Complexity and the Method Used to Present a Complex Item on the Face of a Financial Statement on Nonprofessional Investors` Judgments , Linda Gale Ragland

Theses/Dissertations from 2010 2010

Two Essays on Information Ambiguity and Informed Traders’ Trade-Size Choice , Ziwei Xu

Theses/Dissertations from 2008 2008

Two Essays on the Conflict of Interests within the Financial Services Industry-- Financial Industry Consolidation: The Motivations and Consequences of the Financial Services Modernization Act (FSMA) and “Down but Not Out” Mutual Fund Manager Turnover within Fund Families , Lonnie Lashawn Bryant

Two Essays on Multiple Directorships , Chia-wei Chen

Two Essays on Financial Condition of Firms , Sanjay Kudrimoti

A Study of Cross-Border Takeovers: Examining the Impact of National Culture on Internalization Benefits, and the Implications of Early Versus Late-Mover Status for Bidders and Their Rivals , Tanja Steigner

Two Essays on Corporate Governance⎯Are Local Directors Better Monitors, and Directors Incentives and Earnings Management , Hong Wan

Theses/Dissertations from 2007 2007

The Role of Ethnic Compatibility in Attitude Formation: Marketing to America’s Diverse Consumers , Cynthia Rodriguez Cano

Two Essays on Venture Capital: What Drives the Underpricing of Venture CapitalBacked IPOs and Do Venture Capitalists Provide Anything More than Money? , Donald Flagg

Two essays on market efficiency: Tests of idiosyncratic risk: informed trading versus noise and arbitrage risk, and agency costs and the underlying causes of mispricing: information asymmetry versus conflict of interests , Jung Chul Park

The impact of management's tone on the perception of management's credibility in forecasting , Robert D. Slater

Uncertainty in the information supply chain: Integrating multiple health care data sources , Monica Chiarini Tremblay

Theses/Dissertations from 2006 2006

Adolescent alcohol use and educational outcomes , Wesley A. Austin

Certificate of need regulation in the nursing home industry: Has it outlived its usefulness? , Barbara J. Caldwell

The impacts of the handoffs on software development: A cost estimation model , Michael Jay Douglas

Using emergent outcome controls to manage dynamic software development , Michael Loyd Harris

The information technology professional's psychological contract viewed through their employment arrangement and the relationship to organizational behaviors , Sandra Kay Newton

The causal effect of alcohol consumption on employment status , Chanvuth Sangchai

The effect of transportation subsidies on urban sprawl , Qing Su

The effects of in-group bias and decision aids on auditors' evidence evaluation , Eileen Zalkin Taylor

The single market and pharmaceutical industry in the European Union: Is there any evidence of price convergence? , Aysegul Timur

A structural approach to the study of intra-organizational coalitions , Dean T. Walsh

Advanced Search

• Email Notifications and RSS
• All Collections
• USF Faculty Publications
• Open Access Journals
• Conferences and Events
• Theses and Dissertations
• Textbooks Collection

Useful Links

• Rights Information
• SelectedWorks
• Submit Research

Home | About | Help | My Account | Accessibility Statement | Language and Diversity Statements

Privacy Copyright

PhD Help in Database Management System

A comprehensive course  for obtaining a Ph.D. degree in Database Management System gives an opportunity to focus on database system development, maintenance, and management. While all Ph.D. programs are research oriented, the field of database management systems specifically needs more focus on qualitative research methods and statistics. The most effective way to use information technologies focuses on risk management, system analysis, and process models. In addition, database management system provides growth in sole administration, management, and development of database.

The scholar has to consider protocol standards for network and database designs. The course includes enterprise database management and database system design for the candidates to conduct their independent research. A database systems option introduces the students to database design, data mining, and the use of database applications in various industries. The other specializations include operating systems, security, and networking of the database through the candidates. The course offers varied career opportunities to the candidates, in either government sector as a university professor or researcher or an administrator or analyst in a private firm.

Some  Unique  Topics  Suggested  By  Us  Are  As  Follows:

• DBMS query optimization approach using enhanced multivariant optimization techniques
• An enhanced approach for performance prediction for workloads using sequence pattern extraction.
• Database management system design for gathering network transmission data for enhancing the network security.
• Improving utilization of DBMS for data based prediction with the aid of machine learning approach.
• Effective database management approach using migration of database from SQL to NoSQL database.

Thesis India offers help to the research candidates for their Ph.D. in Database Management System. With the different database management technologies, users use application programmers, and the database and its management system are on its peak importance for application related purposes. You can get PhD Help in Database Management System by dropping a mail at [email protected] . The researchers here provide a complete package help in your research from the topic selection to prepare for your defense.

Total Price (₹) INR 0

Discount (₹): inr 0, off20 applied - inr 0, amount to pay (₹)  inr 0, recent post.

• Exploring the Future of Business Management Research: 3 Emerging Areas for PhD Students to Pursue April 28, 2023 PhD Thesis
• Thesis Proofreading: Do-it-yourself or Hiring an Editor December 21, 2021 Thesis Proofreading
• How to Deal With the Things That Make You Quit PhD November 18, 2021 PhD Thesis
• SDN – Which Topic to Choose for PhD Project & Research work October 14, 2021 PhD Thesis
• THE STEP-TO-STEP GUIDE TO WRITE A SUCCESSFUL RESEARCH PROPOSAL. June 16, 2021 Research Paper
• Exploring the Future of Business Management Research: 3 Emerging Areas for PhD Students to Pursue
• Thesis Proofreading: Do-it-yourself or Hiring an Editor
• How to Deal With the Things That Make You Quit PhD
• SDN – Which Topic to Choose for PhD Project & Research work
• THE STEP-TO-STEP GUIDE TO WRITE A SUCCESSFUL RESEARCH PROPOSAL.