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Indexing Land Surface for Efficient kNN Query

Summary: Proposes the Surface Index R-tree (SIR-tree) with Tight Surface Index (TSI) and Loose Surface Index (LSI) to support exact skNN on land surfaces. Supports a priori-k-free, incremental search with localized pruning, reporting exact shortest surface paths and outperforming baselines in efficiency and accuracy on real data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9733
Venue
VLDB
Year
2008
Pagerank
0.00011593275
Overall Rank
1,514 | 89.47%
DOI
-

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Showing 6 of 6 cited papers.

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

Rank Cited Paper Year Venue Pagerank
2 R-Trees: A Dynamic Index Structure For Spatial Searching 1984 SIGMOD 0.0032169493
47 Nearest Neighbor Queries 1995 SIGMOD 0.0007015885
389 Query Processing in Spatial Network Databases 2003 VLDB 0.00024620268
598 Voronoi-Based K Nearest Neighbor Search for Spatial Network Databases 2004 VLDB 0.00019474545
601 Influence Sets Based on Reverse Nearest Neighbor Queries 2000 SIGMOD 0.00019375875
1,275 Continuous Nearest Neighbor Search 2002 VLDB 0.00012883899
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