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篇目详细内容 |
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【篇名】 |
Adaptive scheduling for shared window joins over data streams |
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【刊名】 |
Frontiers of Computer Science in China |
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【刊名缩写】 |
Front. Comput. Sci. China |
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【ISSN】 |
1673-7350 |
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【EISSN】 |
1673-7466 |
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【DOI】 |
10.1007/s11704-007-0046-8 |
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【出版社】 |
Higher Education Press and Springer-Verlag |
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【出版年】 |
2007 |
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【卷期】 |
1
卷4期 |
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【页码】 |
468-477
页,共
10
页 |
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【作者】 |
JIN Cheqing;
ZHOU Aoying;
Jeffrey Xu Yu;
Joshua Zhexue Huang;
CAO Feng;
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【关键词】 |
data streams; continuous query; shared window joins |
【摘要】 |
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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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