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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
418
Venue
CIDR
Year
2021
Pagerank
4.4797417e-05
Overall Rank
8,638 | 39.91%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
5,572 The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data 2023 SIGMOD 5.4277273e-05
8,263 Raster Intervals: An Approximation Technique for Polygon Intersection Joins 2023 SIGMOD 4.5464722e-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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