Organization: Nagoya University
Email:ishikawa@i.nagoya-u.ac.jp
Title: Pattern Matching over Probabilistic Data Streams
Abstract
As the development of sensor and machine learning technologies has progressed, it has become increasingly important to detect patterns from probabilistic data streams. In this talk, we focus on complex event processing based on pattern matching. When we apply pattern matching to probabilistic data streams, numerous matches may be detected at the same time interval because of the uncertainty of data. Although existing methods distinguish between such matches, they may derive inappropriate results when some of the matches correspond to the real-world event that has occurred during the time interval.
The first topic is how to group matches for detecting complex events. Our methods output groups that indicate the occurrence of complex events during the given time intervals. We first describe the definition of groups based on temporal overlap, and propose two grouping algorithms. Then, we propose an efficient approach for calculating the occurrence probabilities of groups by using deterministic finite automata that are generated from the query patterns.
The second topic is based on the new criterion to detect meaningful matches. The criterion is based on an information-theoretic concept. For that purpose, we introduce pattern matching semantics over probabilistic data streams and show an automaton-based efficient implementation method.
Bio
Yoshiharu
Ishikawa received the B.Eng., M.Eng., and Dr.Eng. degrees in computer
science, all from University of Tsukuba in 1989, 1991, and 1994,
respectively. He joined Nara Institute of Science and
Technology(NAIST) as an assistant professor in April 1994. In April
1999, he moved to University of Tsukuba and worked as an assistant
professor
and an associate professor. From April 2006, he is a
full professor in Nagoya University. Currently he belongs to Graduate
School of Informatics, Nagoya University. His research interests
include databases, especially in spatial and spatio-temporal
databases, data stream management, mobile databases, and scientific
databases. He was a visiting researcher of University of Maryland and
Carnegie Mellon University from 1998 to 1999. He is a member of ACM,
IEEE CS, IEICE, IPSJ, and DBSJ. He is now working as a general chair
for VLDB 2020 in Tokyo.
