DBScholar

Back to papers

Efficient Spatial Sampling of Large Geographical Tables

Summary: Formalizes thinning of geospatial data as an integer-programming optimization for region- and zoom-aware sampling. Proposes DFS-based maximality on a spatial tree; adds a fast randomized method for point datasets; validated on Google Maps Fusion Tables. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4585
Venue
SIGMOD
Year
2012
Pagerank
7.765466e-05
Overall Rank
3,094 | 78.78%
DOI
10.1145/2213836.2213859

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{sarma_sigmod12,
        title = {{Efficient Spatial Sampling of Large Geographical Tables}},
        author = {Sarma, Anish Das and Lee, Hongrae and Gonzalez, Hector and Madhavan, Jayant and Halevy, Alon},
        series = {{SIGMOD} '12},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2213836.2213859},
        url = {https://dl.acm.org/doi/10.1145/2213836.2213859},
        year = {2012}
}

Incoming Citations (Sorted by Pagerank)

Showing 10 of 10 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

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

Previous Page 1 / 1 Next

Semantically Similar Papers