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Processing Top-k Join Queries

Summary: Introduces incremental lower/upper-bound and expected-score computation for top-k join results under sorted or random tuple access. An adaptive table-access ordering minimizes wasted evaluation of partially explored candidates, accelerating branch-and-bound over web-accessible databases. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
h7919d2c1bca71134
Venue
VLDB
Year
2010
Pagerank
5.4834169e-05
Overall Rank
7,615 | 48.81%
DOI
10.14778/1920841.1920949

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{wu_vldb10,
        title = {{Processing Top-k Join Queries}},
        author = {Wu, Minji and Berti-Équille, Laure and Marian, Amélie and Procopiuc, Cecilia M. and Srivastava, Divesh},
        journal = {PVLDB},
        series = {{VLDB} '10},
        volume = {3},
        number = {1},
        pages = {860--871},
        doi = {10.14778/1920841.1920949},
        url = {https://doi.org/10.14778/1920841.1920949},
        year = {2010}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
4,752 Optimal Join Algorithms Meet Top-k 2020 SIGMOD 6.434561e-05
5,482 Beyond Equi-joins: Ranking, Enumeration and Factorization 2021 VLDB 6.111411e-05
8,048 Progressive Join Algorithms Considering User Preference 2021 CIDR 5.4004443e-05
9,681 Rank Join Queries in NoSQL Databases 2014 VLDB 5.1427987e-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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