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Selectivity Estimation for Spatio-Temporal Queries to Moving Objects

Summary: Spatio-temporal selectivity estimation for moving objects to guide query optimization. Introduces analytical formulas that account for future positions, with experiments showing 9–23% average error vs 14–85% for spatial-only methods on Tiger/Lines. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3370
Venue
SIGMOD
Year
2002
Pagerank
6.4100417e-05
Overall Rank
4,146 | 71.16%
DOI
-

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Showing 8 of 8 cited papers.

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

Rank Cited Paper Year Venue Pagerank
269 Fast Incremental Maintenance of Approximate Histograms 1997 VLDB 0.00029656549
631 Indexing the Positions of Continuously Moving Objects 2000 SIGMOD 0.00018935493
668 The Sequoia 2000 Storage Benchmark 1993 SIGMOD 0.00018430721
1,002 On Indexing Mobile Objects 1999 PODS 0.00014702555
1,766 Indexing Moving Points (Extended Abstract) 2000 PODS 0.000106236
2,053 Selectivity Estimation in Spatial Databases 1999 SIGMOD 9.6728745e-05
2,499 The MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries 2001 VLDB 8.646204e-05
6,769 A Data Model and Data Structures for Moving Objects Databases 2000 SIGMOD 4.9320699e-05
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