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Optimization of Continuous Queries with Shared Expensive Filters

Summary: Adaptive shared execution of expensive filters across many continuous conjunctive queries beats any fixed-order strategy as filter costs rise, despite adaptivity overhead. Shows optimal adaptive planning is NP-hard and ln m-inapproximable; gives a greedy algorithm with O(log^2 m log n) approximation and a precomputation technique to cut runtime overhead. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1430
Venue
PODS
Year
2007
Pagerank
6.7021508e-05
Overall Rank
4,441 | 69.54%
DOI
10.1145/1265530.1265561

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{munagala_pods07,
        address = {New York, NY, USA},
        series = {{PODS} '07},
        title = {{Optimization of Continuous Queries with Shared Expensive Filters}},
        url = {https://dl.acm.org/doi/10.1145/1265530.1265561},
        doi = {10.1145/1265530.1265561},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Munagala, Kamesh and Srivastava, Utkarsh and Widom, Jennifer},
        year = {2007}
}

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