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The Case for Distance-Bounded Spatial Approximations

Summary: Advocates returning final query results directly from fine-grained spatial approximations (no exact geometry rechecks) with provable distance-bounded error. Enables controllable accuracy–performance tradeoffs for interactive, imprecise geospatial workloads using modern hardware. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
417
Venue
CIDR
Year
2021
Pagerank
5.4012875e-05
Overall Rank
8,614 | 40.91%
DOI
-

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{zacharatou_cidr21,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '21},
        title = {{The Case for Distance-Bounded Spatial Approximations}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Zacharatou, Eleni Tzirita and Kipf, Andreas and Sabek, Ibrahim and Pandey, Varun and Doraiswamy, Harish and Markl, Volker},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
4,751 The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data 2023 SIGMOD 6.5241784e-05
8,061 Raster Intervals: An Approximation Technique for Polygon Intersection Joins 2023 SIGMOD 5.4953977e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 8 of 8 cited papers.

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

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