Optimizing cost of continuous overlapping queries over data streams by filter adaption

Xie, Qing, Zhang, Xiangliang, Li, Zhixu and Zhou, Xiaofang (2016) Optimizing cost of continuous overlapping queries over data streams by filter adaption. IEEE Transactions on Knowledge and Data Engineering, 28 5: 1258-1271. doi:10.1109/TKDE.2016.2516541


Author Xie, Qing
Zhang, Xiangliang
Li, Zhixu
Zhou, Xiaofang
Title Optimizing cost of continuous overlapping queries over data streams by filter adaption
Journal name IEEE Transactions on Knowledge and Data Engineering   Check publisher's open access policy
ISSN 1041-4347
1558-2191
Publication date 2016-05-01
Year available 2016
Sub-type Article (original research)
DOI 10.1109/TKDE.2016.2516541
Open Access Status Not Open Access
Volume 28
Issue 5
Start page 1258
End page 1271
Total pages 14
Place of publication Piscataway, United States
Publisher Institute of Electrical and Electronics Engineers
Collection year 2017
Language eng
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 the 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.
Keyword Cost optimization
Data streams
Filter adaption
Overlapping queries
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
 
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