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On Joining and Caching Stochastic Streams

Summary: A statistics-aware caching framework for joining stochastic streams with bounded memory aims to maximize the expected number of output tuples. It shows that full lookahead can be suboptimal, derives a dominance condition between candidate tuples, and provides a heuristic that matches optimal behavior under that condition, with empirical gains and a reduction of static paging to stream joins. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3649
Venue
SIGMOD
Year
2005
Pagerank
4.9070864e-05
Overall Rank
6,853 | 52.33%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
4,133 Memory-Limited Execution of Windowed Stream Joins 2004 VLDB 6.4196026e-05
5,150 Efficient Join Synopsis Maintenance for Data Warehouse 2020 SIGMOD 5.6626586e-05
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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