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Towards Robust Indexing for Ranked Queries

Summary: Introduces robust layered indexes for top-k queries, minimizing tuples in the first k layers for every linear ranking function. Characterizes robustness exactly and provides polynomial-time exact and dimension-aware approximate constructions with improved empirical performance. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
h2545d3cae95a2358
Venue
VLDB
Year
2006
Pagerank
7.6867401e-05
Overall Rank
3,068 | 79.38%
DOI
-

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{xin_vldb06,
        title = {{Towards Robust Indexing for Ranked Queries}},
        author = {Xin, Dong and Chen, Chen and Han, Jiawei},
        journal = {PVLDB},
        series = {{VLDB} '06},
        pages = {235--246},
        year = {2006}
}

Incoming Citations (Sorted by Pagerank)

Showing 12 of 12 citing papers.

Rank Citing Paper Year Venue Pagerank
942 SPARK: Top-k Keyword Query in Relational Databases 2007 SIGMOD 0.00012956137
2,955 Answering Why-not Questions on Reverse Top-k Queries 2015 VLDB 7.8102237e-05
4,614 Branch-and-Bound Algorithm for Reverse Top-k Queries 2013 SIGMOD 6.5014577e-05
4,831 Efficient Computation of Regret-ratio Minimizing Set: A Compact Maxima Representative 2017 SIGMOD 6.3897334e-05
5,353 Why Not Yet: Fixing a Top-k Ranking that Is Not Fair to Individuals 2023 VLDB 6.1637765e-05
5,913 Reverse k-Ranks Query 2014 VLDB 5.9500931e-05
6,716 RRR: Rank-Regret Representative 2019 SIGMOD 5.6993087e-05
7,394 Efficient and Generic Evaluation of Ranked Queries 2011 SIGMOD 5.5370795e-05
11,712 rkHit: Representative Query with Uncertain Preference 2023 SIGMOD 4.9793485e-05
12,150 Top-k Queries over Digital Traces 2019 SIGMOD 4.9793485e-05
12,376 Query Reranking As A Service 2016 VLDB 4.9793485e-05
12,622 Answering Top-k Queries Over a Mixture of Attractive and Repulsive Dimensions 2012 VLDB 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 8 of 8 cited papers.

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

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