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Proximity Rank Join

Summary: Proximity rank join: relations with scores and feature vectors; return top-K combos closest to a target and to each other. A tight bound guarantees instance-optimal I/O and drives adaptive pulling; experiments show gains vs HRJN-based methods. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10091
Venue
VLDB
Year
2010
Pagerank
6.4726748e-05
Overall Rank
4,072 | 71.71%
DOI
-

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
2,474 Top-k Bounded Diversification 2012 SIGMOD 8.6956353e-05
12,190 Search Computing: Multi-domain Search on Ranked Data 2011 SIGMOD 4.1905499e-05
12,199 Efficient Rank Join with Aggregation Constraints 2011 VLDB 4.1905499e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

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

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