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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
10281
Venue
VLDB
Year
2010
Pagerank
6.1384844e-05
Overall Rank
5,640 | 61.31%
DOI
10.14778/1920841.1920889

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{martinenghi_vldb10,
        title = {{Proximity Rank Join}},
        author = {Martinenghi, Davide and Tagliasacchi, Marco},
        journal = {PVLDB},
        series = {{VLDB} '10},
        volume = {3},
        number = {1},
        pages = {352--363},
        doi = {10.14778/1920841.1920889},
        url = {https://doi.org/10.14778/1920841.1920889},
        year = {2010}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
2,259 Top-k Bounded Diversification 2012 SIGMOD 8.8529269e-05
12,377 Search Computing: Multi-domain Search on Ranked Data 2011 SIGMOD 5.093636e-05
12,386 Efficient Rank Join with Aggregation Constraints 2011 VLDB 5.093636e-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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