Advertisement
Published in:
2015 | OriginalPaper | Chapter
Grouping Methods for Pattern Matching in Probabilistic Data Streams
Authors : Kento Sugiura, Yoshiharu Ishikawa, Yuya Sasaki
Published in: Database Systems for Advanced Applications
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
In recent years,
complex event processing
has attracted considerable interest in research and industry.
Pattern matching
is used to find complex events in data streams. In probabilistic data streams, however, the system may find multiple matches in a given time interval. This may result in inappropriate matches, because multiple matches may correspond to a single event. We therefore propose
grouping methods
of matches for probabilistic data streams, and call such merged matches a
group
. We describe the definitions and generation methods of groups, propose an efficient approach for calculating an occurrence probability of a group, and compare the proposed approach with a naïve one by experiment. The results demonstrate the properties and effectiveness of the proposed method.