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CPR: Complex Pattern Ranking for Evaluating Top-k Pattern Queries over Event Streams


Abstract Most existing approaches to complex event processing over streaming data rely on the assumption that the matches to the queries are rare and that the goal of the system is to identify these few matches within the incoming deluge of data. In many applications, such as stock market analysis and user credit card purchase pattern monitoring, however the matches to the user queries are in fact plentiful and the system has to efficiently sift through these many matches to locate only the few most preferable matches. In this work, we propose a complex pattern ranking (CPR) framework for specifying top-k pattern queries over streaming data, present new algorithms to support top-k pattern queries in data streaming environments, and verify the effect... (more)
Created Date 2011
Contributor Wang, Xinxin (Author) / Candan, K. Selcuk (Advisor) / Chen, Yi (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Subject Computer Science / complex event processing / pattern ranking / top-k query
Type Masters Thesis
Extent 60 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note M.S. Computer Science 2011
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis