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Efficient Processing of Top-k Spatial Preference Queries

Summary: Maps data–feature pairs into a distance-score space to identify the minimal pair set sufficient for any top-k spatial preference query. A neighborhood-free query algorithm with efficient materialization and maintenance then reduces I/Os and speeds up execution versus state-of-the-art methods. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10321
Venue
VLDB
Year
2011
Pagerank
0.00012176993
Overall Rank
1,403 | 90.25%
DOI
-

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