Vijayalakshmi Kakulapati

Sreenidhi Institute of Science and Technology, India

Prof. Vijayalakshmi Kakulapati received a Ph.D. in Computer Science and Engineering from Jawaharlal Nehru Technological University (JNTU), Hyderabad. She is currently a professor in the Department of Information Technology, Sreenidhi Institute of Science and Technology, Hyderabad. She has twenty-six years of industry and teaching experience and is a member of various professional bodies, including the Institute of Electrical and Electronics Engineer (IEEE), Association for Computing Machinery (ACM), Computer Science Teachers Association (CSTA), LMISTE, LMCSI, International Association of Computer Science and Information Technology (IACSIT), FIETE, and more. She has more than 160 publications in national and international journals and conferences, 30 book chapters, and 4 books to her credit. She has received numerous awards, including Excellence in Research, Best Reviewer, appreciation awards, and more. Her areas of research include theoretical and practical information retrieval problems as well as machine learning applied to large-scale textual applications. Her research has focused on retrieval models, query/document representations, term weighting, term proximity models, and learning to rank (machine-learned ranking functions). She is also passionate about seeing research problems applied to real-world problems, especially those dealing with large, complex data sets. Along these lines, she is working with evaluating and designing novel search algorithms for web search and summarization. Currently, Dr. Kakulapati is working with big data analytics, health informatics, the Internet of Things, deep learning, artificial intelligence, and data sciences.

Vijayalakshmi Kakulapati

1books edited

2chapters authored

Latest work with IntechOpen by Vijayalakshmi Kakulapati

Data is often open to all users and sharers. Governments provide data on publicly available websites and this data may pertain to specific regions or be aggregate data on national or international issues. Data that is in the public domain but not in a machine-readable format is considered public data and may only be accessible via a right-of-access request. Maintaining accuracy and management is a major obstacle when it comes to data systems and solutions. Data governance describes the rules, procedures, and responsibilities that outline the data's acquisition, storage, retrieval and use. Data security and privacy refer to safeguards put in place to protect information from being seen, copied, distributed, altered, or destroyed without permission. Data integration and interoperability involve combining and exchanging data from many sources, systems, and formats, as well as facilitating data sharing and collaboration across various platforms, apps, and organizations. Defining data standards, implementing data quality checks, assigning data ownership and responsibility, and monitoring data performance and utilization are all important steps toward resolving the data quality problem. This book contains two sections. “Trends and Challenges of Open Data” and “Case Studies”. Each section contains three chapters.

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