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Adaptive Sampling for Geometric Problems over Data Streams

Summary: Adaptive, single-pass sampling scheme that maintains at most 2r+1 points to approximate the convex hull of a 2D point stream, enabling many extremal geometric queries over streams. Provable guarantee: hull error O(D/r^2) (D=diameter), yielding optimal bounds and O(log r) or O(r) query time for diameter, hull-distance, separability, etc. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1327
Venue
PODS
Year
2004
Pagerank
5.4323688e-05
Overall Rank
8,404 | 42.35%
DOI
10.1145/1055558.1055595

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{hershberger_pods04,
        address = {New York, NY, USA},
        series = {{PODS} '04},
        title = {{Adaptive Sampling for Geometric Problems over Data Streams}},
        url = {https://dl.acm.org/doi/10.1145/1055558.1055595},
        doi = {10.1145/1055558.1055595},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Hershberger, John and Suri, Subhash},
        year = {2004}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
5,193 Sampling Algorithms in a Stream Operator 2005 SIGMOD 6.3238562e-05
7,379 Comparing Synopsis Techniques for Approximate Spatial Data Analysis 2019 VLDB 5.629255e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

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

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