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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
hb9dd99de004a3edd
Venue
VLDB
Year
2007
Pagerank
6.6122752e-05
Overall Rank
4,408 | 70.37%
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
3,326 Sliding-Window Top-k Queries on Uncertain Streams 2008 VLDB 7.4227632e-05
3,540 Tighter Estimation using Bottom k Sketches 2008 VLDB 7.2161972e-05
3,963 Using Trees to Depict a Forest 2009 VLDB 6.8922952e-05
5,229 Robust and Efficient Algorithms for Rank Join Evaluation 2009 SIGMOD 6.2196057e-05
6,027 Answering Top-k Representative Queries on Graph Databases 2014 SIGMOD 5.9112117e-05
8,814 ARCube: Supporting Ranking Aggregate Queries in Partially Materialized Data Cubes 2008 SIGMOD 5.2710039e-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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