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Parallel Algorithms for High-dimensional Proximity Joins

Summary: Proposes a parallel multidimensional proximity-join using the epsilon-kdB tree, with empirical comparison to space-partitioning approaches. Demonstrates scalable performance on a shared-nothing IBM SP2, handles data skew, and applies to time-series similarity mining. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
8418
Venue
VLDB
Year
1997
Pagerank
4.8226285e-05
Overall Rank
7,133 | 50.38%
DOI
-

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
1,631 Incremental Distance Join Algorithms for Spatial Databases 1998 SIGMOD 0.00011078269
6,507 Similarity Join over Array Data 2016 SIGMOD 5.0337166e-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.

Rank Cited Paper Year Venue Pagerank
65 Fast Subsequence Matching in Time-Series Databases 1994 SIGMOD 0.00062029383
148 Efficient Processing of Spatial Joins Using R-trees 1993 SIGMOD 0.00041182766
216 A Class of Data Structures for Associative Searching 1984 PODS 0.00033542705
290 Linear Clustering of Objects with Multiple Attributes 1990 SIGMOD 0.00028919734
362 Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases 1995 VLDB 0.00025770385
925 Partition Based Spatial-Merge Join 1996 SIGMOD 0.00015264328
1,174 Spatial Hash-Joins 1996 SIGMOD 0.00013486418
3,457 Size Separation Spatial Join 1997 SIGMOD 7.0755358e-05
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