Publication:
E-Cube: multi-dimensional event sequence analysis using hierarchical pattern query sharing

dc.contributor.authorLiu, M.
dc.contributor.authorRundensteiner, E.
dc.contributor.authorGreenfield, K.
dc.contributor.authorGupta, C.
dc.contributor.authorWang, S.
dc.contributor.authorArı, İsmail
dc.contributor.authorMehta, A.
dc.contributor.departmentComputer Science
dc.contributor.ozuauthorARI, Ismail
dc.date.accessioned2016-02-11T06:46:13Z
dc.date.available2016-02-11T06:46:13Z
dc.date.issued2011-06-12
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.
dc.description.abstractMany modern applications, including online financial feeds, tag-based mass transit systems and RFID-based supply chain management systems transmit real-time data streams. There is a need for event stream processing technology to analyze this vast amount of sequential data to enable online operational decision making. Existing techniques such as traditional online analytical processing (OLAP) systems are not designed for real-time pattern-based operations, while state-of-the-art Complex Event Processing (CEP) systems designed for sequence detection do not support OLAP operations. We propose a novel E-Cube model which combines CEP and OLAP techniques for efficient multi-dimensional event pattern analysis at different abstraction levels. Our analysis of the interrelationships in both concept abstraction and pattern refinement among queries facilitates the composition of these queries into an integrated E-Cube hierarchy. Based on this E-Cube hierarchy, strategies of drill-down (refinement from abstract to more specific patterns) and of roll-up (generalization from specific to more abstract patterns) are developed for the efficient workload evaluation. Our proposed execution strategies reuse intermediate results along both the concept and the pattern refinement relationships between queries. Based on this foundation, we design a cost-driven adaptive optimizer called Chase, that exploits the above reuse strategies for optimal E-Cube hierarchy execution. Our experimental studies comparing alternate strategies on a real world financial data stream under different workload conditions demonstrate the superiority of the Chase method. In particular, our Chase execution in many cases performs ten fold faster than the state-of-the art strategy for real stock market query workloads.
dc.description.sponsorshipHP Labs Innovation Research Program ; NSF ; TÜBİTAK
dc.identifier.doi10.1145/1989323.1989416
dc.identifier.endpage900
dc.identifier.isbn978-1-4503-0661-4
dc.identifier.scopus2-s2.0-79959948195
dc.identifier.startpage889
dc.identifier.urihttp://hdl.handle.net/10679/1960
dc.identifier.urihttps://doi.org/10.1145/1989323.1989416
dc.language.isoengen_US
dc.peerreviewedyes
dc.publicationstatuspublisheden_US
dc.publisherACM
dc.relationinfo:turkey/grantAgreement/TUBITAK/109E194
dc.relation.ispartofSIGMOD '11 Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
dc.relation.publicationcategoryInternational
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsComplex event processing
dc.subject.keywordsOLAP
dc.subject.keywordsStreaming
dc.subject.keywordsOptimization
dc.subject.keywordsAlgorithm
dc.titleE-Cube: multi-dimensional event sequence analysis using hierarchical pattern query sharingen_US
dc.typeConference paperen_US
dspace.entity.typePublication
relation.isOrgUnitOfPublication85662e71-2a61-492a-b407-df4d38ab90d7
relation.isOrgUnitOfPublication.latestForDiscovery85662e71-2a61-492a-b407-df4d38ab90d7

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