Theory of Data Stream Computing: Where to Go

Summary: Reframes data-management theory for massive, high-rate streams that defy full capture, storage, or communication, advocating principled "work-with-less" algorithms. Surveys advances in stream algorithms, compressed sensing and DSMS and pinpoints open research challenges across computation, communication and storage. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 1552
Venue: PODS
Year: 2011
Pagerank: -
Overall Rank: 13,682 | 6.13%
DOI: 10.1145/1989284.1989314

Incoming Non-self Citations Over Time

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Authors

1. S. Muthukrishnan (Rutgers University)

BibTeX Citation

@inproceedings{muthukrishnan_pods11,
        address = {New York, NY, USA},
        series = {{PODS} '11},
        title = {{Theory of Data Stream Computing: Where to Go}},
        url = {https://dl.acm.org/doi/10.1145/1989284.1989314},
        doi = {10.1145/1989284.1989314},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Muthukrishnan, S.},
        year = {2011}
}

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
2,992	Pan-private Algorithms Via Statistics on Sketches	2011	PODS	7.8836024e-05

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