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Dr. Jaideep Srivastava is a Professor of Computer Science & Engineering at the University of Minnesota. He has established and led a laboratory that has conducted research in databases, multimedia systems, and data mining. He has supervised 23 Ph.D. dissertations and 44 MS theses, and has authored/co-authored over 185 papers in journals and conferences.
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Dr. Srivastava has an active collaboration with the technology industry, both for research and technology transfer, and is an often-invited participant in technical and technology strategy forums. The US federal government has solicited his opinion on computer science research as an expert witness. Dr. Srivastava’s industry experience includes leading data mining at amazon.com, and data warehousing, mining, and reporting at Yodlee. He has provided technology and technology strategy advice to a number of large corporations, including Cargill, United Technologies, IBM, Honeywell, 3M, and Persistent Systems. He has served in an advisory capacity to a number of small companies, including Lancet Software, and Infobionics.
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Professor Srivastava’s current research Interests focus on Web Mining — Application of data mining techniques to Web data. He and his research team are investigating how information about content, structure, and usage of the Web can be mined for knowledge useful to various applications. A critical issue is the modeling of human interaction with the Web. Dr. Srivastava completed his Ph.D. in 1988, in Electrical Engineering and Computer Science at the University of California, Berkeley. He also has an M.S. in Computer Science, also from UC-Berkeley (1985), and the B. Tech. degree in Computer Science from the Indian Institute of Technology, Kanpur, India (1983).
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Research Statement from Dr. Srivastava:
Web Mining - Application of data mining techniques to Web data: We are investigating how information about content, structure, and usage of the Web can be mined for knowledge useful to various applications. A critical issue is the modeling of human interaction with the Web. We believe that page hits are at too fine a granularity to provide useful information and that user behavior must be analyzed at a coarser granularity. Our approach is to group Web page hits into user transactions, based on clustering, which serve as the units of human interaction with the Web. Our ongoing work uses Markov models to approximate the process a user is going through in browsing the Web. Another interesting issue is to mine for interesting usage patterns in Web logs. Hyperlinks in Web pages capture the author's view of pieces of information linked together, while browsing patterns capture the users' view of it. We consider a usage pattern interesting if there is significant disagreement between the two views. We are using the framework of logic with supports to model the beliefs in this environment, and using information about content, structure, and usage of Web pages to estimate the degrees of these beliefs. |
Multimedia System: Multimedia information has had a significant impact on our ability to comprehend the phenomenon producing the information, and this trend continues to accelerate. Providing this information to the user in an effective manner, however, poses a number of significant challenges, including very large data volume, timeliness of data delivery, and overall information quality. Our research approach has been to use (a) the special nature of multimedia information, (b) the nature of infrastructure hardware and software, and (c) the perceptual requirements of end users, to develop a number of mechanisms that can be used in next generation operating systems, networks, and databases for multimedia. |
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