TITLE:
Efficient Techniques and Algorithms for Record Linkage

ABSTRACT:
Abstract: Record linkage is the process of merging data from several sources and identifying records that are associated with the same entities, or individuals, where a unique identifier is not available. Record Linkage has applications in several domains such as master data management, law enforcement, health care, social networking, historical research, multi-modal data analysis, etc. A straightforward algorithm for record linkage would compute the distance between every pair of records and hence take at least quadratic time. In a typical application of interest, the number of records could be in the millions or more. Thus quadratic time algorithms may not be feasible in practice. It is imperative to create novel record linkage algorithms that are very efficient.

In this talk, we survey some of the techniques and algorithms employed in solving the record linkage problem. One of the popular techniques used to speed up record linkage algorithms is blocking. Blocking can be thought of as a step in which potentially unrelated record pairs are pruned from distance calculations. A large number of blocking techniques have been proposed in the literature. We will introduce some of them. In addition, we will consider parallel algorithms for record linkage as well.

BIO:
Sanguthevar Rajasekaran received his M.E. degree in Automation from the Indian Institute of Science (Bangalore) in 1983, and his Ph.D. degree in Computer Science from Harvard University in 1988. Currently, he is the Head of the CSE Department, Board of Trustees Distinguished Professor, and Pratt & Whitney Chair Professor of CSE at the University of Connecticut. Before joining UConn, he served as a faculty member in the CISE Department of the University of Florida and in the CIS Department of the University of Pennsylvania. During 2000-2002 he was the Chief Scientist for Arcot Systems. His research interests include Big Data, AI and Machine Learning, Bioinformatics, Algorithms, Data Mining, Randomized Computing, and HPC. He has published over 350 research articles in journals and conferences. He has co-authored two texts on algorithms and co-edited six books on algorithms and related topics. He has been awarded numerous research grants from such agencies as NSF, NIH, US Census Bureau, CIA, DARPA, Industry, and DHS (totaling more than $22M). He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), the American Association for the Advancement of Science (AAAS), the American Institute for Medical and Biological Engineering (AIMBE), and the Asia-Pacific Artificial Intelligence Association (AIAA). He is also an elected member of the Connecticut Academy of Science and Engineering.