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Adaptive scheduling for shared window joins over data streams |
| JIN Cheqing1, ZHOU Aoying2, Jeffrey Xu Yu3, Joshua Zhexue Huang4, CAO Feng5 |
| 1.Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China; 2.Department of Computer Science and Engineering, Fudan University, Shanghai 200433, China; 3.Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, China; 4.E-Business Technology Institute, The University of Hong Kong, China; 5.IBM China-Research Lab, Bejing 100094, China; |
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Abstract Recently a few Continuous Query systems have been developed to cope with applications involving continuous data streams. At the same time, numerous algorithms are proposed for better performance. A recent work on this subject was to define scheduling strategies on shared window joins over data streams from multiple query expressions. In these strategies, a tuple with the highest priority is selected to process from multiple candidates. However, the performance of these static strategies is deeply influenced when data are bursting, because the priority is determined only by static information, such as the query windows, arriving order, etc. In this paper, we propose a novel adaptive strategy where the priority of a tuple is integrated with realtime information. A thorough experimental evaluation has demonstrated that this new strategy can outperform the existing strategies.
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Issue Date: 05 December 2007
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