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Efficiently Answering Top-k Typicality Queries on Large Databases

Summary: Introduces top-k typicality queries, including simple and discriminative typicality, to identify representative or class-distinguishing objects. Avoids quadratic exact computation via linear randomized tournaments and VP-tree/Local Typicality Tree approximations with quality guarantees. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
9830
Venue
VLDB
Year
2007
Pagerank
6.7617825e-05
Overall Rank
4,321 | 70.36%
DOI
-

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{hua_vldb07,
        title = {{Efficiently Answering Top-k Typicality Queries on Large Databases}},
        author = {Hua, Ming and Pei, Jian and Fu, Ada W. C. and Lin, Xuemin and Leung, Ho-Fung},
        journal = {PVLDB},
        series = {{VLDB} '07},
        pages = {890},
        year = {2007}
}

Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

Rank Citing Paper Year Venue Pagerank
3,481 Tighter Estimation using Bottom k Sketches 2008 VLDB 7.376137e-05
3,599 Sliding-Window Top-k Queries on Uncertain Streams 2008 VLDB 7.2710351e-05
3,881 Using Trees to Depict a Forest 2009 VLDB 7.0490549e-05
5,138 Robust and Efficient Algorithms for Rank Join Evaluation 2009 SIGMOD 6.3495536e-05
5,965 Answering Top-k Representative Queries on Graph Databases 2014 SIGMOD 6.0256454e-05
8,649 ARCube: Supporting Ranking Aggregate Queries in Partially Materialized Data Cubes 2008 SIGMOD 5.3916641e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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

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