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The Impact of Global Clustering on Spatial Database Systems

Summary: Proposes cluster organization, a global clustering with no algorithmic overhead. Real-data experiments show up to 4x faster spatial joins and window queries, while preserving insertions and selective point queries, outperforming non-global models. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
8191
Venue
VLDB
Year
1994
Pagerank
4.4832771e-05
Overall Rank
8,599 | 40.24%
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
479 Multi-Step Processing of Spatial Joins 1994 SIGMOD 0.00022182486
5,864 Benchmarking Spatial Join Operations with Spatial Output 1995 VLDB 5.2947135e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 12 of 12 cited papers.

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

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