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dc.contributor.authorXie, Qing
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorLi, Zhixu
dc.contributor.authorZhou, Xiaofang
dc.date.accessioned2016-01-13T09:43:59Z
dc.date.available2016-01-13T09:43:59Z
dc.date.issued2016-01-12
dc.identifier.citationOptimizing Cost of Continuous Overlapping Queries over Data Streams by Filter Adaption 2016:1 IEEE Transactions on Knowledge and Data Engineering
dc.identifier.issn1041-4347
dc.identifier.doi10.1109/TKDE.2016.2516541
dc.identifier.urihttp://hdl.handle.net/10754/593341
dc.description.abstractThe problem we aim to address is the optimization of cost management for executing multiple continuous queries on data streams, where each query is defined by several filters, each of which monitors certain status of the data stream. Specially the filter can be shared by different queries and expensive to evaluate. The conventional objective for such a problem is to minimize the overall execution cost to solve all queries, by planning the order of filter evaluation in shared strategy. However, in streaming scenario, the characteristics of data items may change in process, which can bring some uncertainty to the outcome of individual filter evaluation, and affect the plan of query execution as well as the overall execution cost. In our work, considering the influence of the uncertain variation of data characteristics, we propose a framework to deal with the dynamic adjustment of filter ordering for query execution on data stream, and focus on the issues of cost management. By incrementally monitoring and analyzing the results of filter evaluation, our proposed approach can be effectively adaptive to the varied stream behavior and adjust the optimal ordering of filter evaluation, so as to optimize the execution cost. In order to achieve satisfactory performance and efficiency, we also discuss the trade-off between the adaptivity of our framework and the overhead incurred by filter adaption. The experimental results on synthetic and two real data sets (traffic and multimedia) show that our framework can effectively reduce and balance the overall query execution cost and keep high adaptivity in streaming scenario.
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7378511
dc.rights(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectOverlapping queries
dc.subjectcost optimization
dc.subjectdata streams
dc.subjectfilter adaption
dc.titleOptimizing Cost of Continuous Overlapping Queries over Data Streams by Filter Adaption
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentMaterial Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalIEEE Transactions on Knowledge and Data Engineering
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Computer Science and Technology, Soochow University, China
dc.contributor.institutionSchool of ITEE, the University of Queensland, Brisbane, Australia, and the School of Computer Science and Technology, Soochow University, China
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personXie, Qing
kaust.personZhang, Xixiang
refterms.dateFOA2018-06-13T13:34:01Z
dc.date.published-online2016-01-12
dc.date.published-print2016-05-01


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