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

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    Type
    Article
    Authors
    Xie, Qing cc
    Zhang, Xiangliang cc
    Li, Zhixu
    Zhou, Xiaofang
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Material Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2016-01-12
    Online Publication Date
    2016-01-12
    Print Publication Date
    2016-05-01
    Permanent link to this record
    http://hdl.handle.net/10754/593341
    
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    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.
    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
    DOI
    10.1109/TKDE.2016.2516541
    Additional Links
    http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7378511
    ae974a485f413a2113503eed53cd6c53
    10.1109/TKDE.2016.2516541
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Material Science and Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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