Evaluating Probabilistic Queries over Uncertain Matching
Prof. Reynold C.K.Cheng
A matching between two database schemas generated by machine learning techniques (e.g., COMA++) is often uncertain. Handling the uncertainty of schema matching has recently raised a lot of research interest, because the quality of applications relies on the matching result. We study query evaluation over an inexact schema matching which is represented as a set of “possible mappings”, as well as the probabilities that they are correct. Since the number of possible mappings can be large, evaluating queries through these mappings can be expensive. By observing that the possible mappings between two schemas often exhibit a high degree of overlap, we develop efficient solutions. We also present a fast algorithm to compute answers with the k highest probabilities. An extensive evaluation on real schemas shows that our approaches improve query performance by almost an order of magnitude.
Dr. Reynold Cheng is an Associate Professor of the Department of Computer Science in the University of Hong Kong. He is the Chair of the Department Research Postgraduate Committee, and is the Vice Chairperson of the ACM (Hong Kong Chapter).He is also a guest editor for a special issue in TKDE.He has served as PC members and reviewer for international conferences and journals including TODS, TKDE, TMC, VLDBJ, IS, DKE, KAIS, VLDB, ICDE, ICDM, DEXA and DASFAA.