Optimizing Cost of Continuous Overlapping Queries over Data Streams by Filter Adaption

Handle URI:
http://hdl.handle.net/10754/593341
Title:
Optimizing Cost of Continuous Overlapping Queries over Data Streams by Filter Adaption
Authors:
Xie, Qing ( 0000-0003-4530-588X ) ; Zhang, Xiangliang ( 0000-0002-3574-5665 ) ; Li, Zhixu; Zhou, Xiaofang
Abstract:
The 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Optimizing Cost of Continuous Overlapping Queries over Data Streams by Filter Adaption 2016:1 IEEE Transactions on Knowledge and Data Engineering
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Knowledge and Data Engineering
Issue Date:
12-Jan-2016
DOI:
10.1109/TKDE.2016.2516541
Type:
Article
ISSN:
1041-4347
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7378511
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorXie, Qingen
dc.contributor.authorZhang, Xiangliangen
dc.contributor.authorLi, Zhixuen
dc.contributor.authorZhou, Xiaofangen
dc.date.accessioned2016-01-13T09:43:59Zen
dc.date.available2016-01-13T09:43:59Zen
dc.date.issued2016-01-12en
dc.identifier.citationOptimizing Cost of Continuous Overlapping Queries over Data Streams by Filter Adaption 2016:1 IEEE Transactions on Knowledge and Data Engineeringen
dc.identifier.issn1041-4347en
dc.identifier.doi10.1109/TKDE.2016.2516541en
dc.identifier.urihttp://hdl.handle.net/10754/593341en
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.en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7378511en
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.en
dc.subjectOverlapping queriesen
dc.subjectcost optimizationen
dc.subjectdata streamsen
dc.subjectfilter adaptionen
dc.titleOptimizing Cost of Continuous Overlapping Queries over Data Streams by Filter Adaptionen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalIEEE Transactions on Knowledge and Data Engineeringen
dc.eprint.versionPost-printen
dc.contributor.institutionSchool of Computer Science and Technology, Soochow University, Chinaen
dc.contributor.institutionSchool of ITEE, the University of Queensland, Brisbane, Australia, and the School of Computer Science and Technology, Soochow University, Chinaen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorXie, Qingen
kaust.authorZhang, Xixiangen
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