2017年4月1日
Grouping Methods for Pattern Matching over Probabilistic Data Streams
IEICE Transactions on Information and Systems
- ,
- ,
- 巻
- E100.D
- 号
- 4
- 開始ページ
- 718
- 終了ページ
- 729
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1587/transinf.2016dap0014
- 出版者・発行元
- 一般社団法人 電子情報通信学会
<p>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 paper, 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. Thus, we propose two grouping methods for matches. Our methods output groups that indicate the occurrence of complex events during the given time intervals. In this paper, first we describe the definition of groups based on temporal overlap, and propose two grouping algorithms, introducing the notions of complete overlap and single overlap. 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. Finally, we empirically evaluate the effectiveness of our methods by applying them to real and synthetic datasets.</p>
- リンク情報
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- DOI
- https://doi.org/10.1587/transinf.2016dap0014
- DBLP
- https://dblp.uni-trier.de/rec/journals/ieicet/SugiuraI017
- CiNii Articles
- http://ci.nii.ac.jp/naid/130005529927
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000399371100014&DestApp=WOS_CPL
- URL
- https://www.jstage.jst.go.jp/article/transinf/E100.D/4/E100.D_2016DAP0014/_pdf
- ID情報
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- DOI : 10.1587/transinf.2016dap0014
- ISSN : 0916-8532
- eISSN : 1745-1361
- DBLP ID : journals/ieicet/SugiuraI017
- CiNii Articles ID : 130005529927
- Web of Science ID : WOS:000399371100014