Approximate Join Processing Over Data Streams
Summary: Approximate sliding-window joins in data streams by dropping tuples. Proposes offline-optimal and fast online algorithms to minimize output tuples; surveys architecture and quality metrics; introduces Archive-metric for completing joins after archiving. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Abhinandan Das (Cornell University)
- 2. Johannes Gehrke (Cornell University)
- 3. Mirek Riedewald (Cornell University)
BibTeX Citation
@inproceedings{das_sigmod03,
title = {{Approximate Join Processing Over Data Streams}},
author = {Das, Abhinandan and Gehrke, Johannes and Riedewald, Mirek},
series = {{SIGMOD} '03},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/872757.872765},
url = {https://dl.acm.org/doi/10.1145/872757.872765},
year = {2003}
}
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