IO-Top-k: Index-access Optimized Top-k Query Processing
Summary: IO-Top-k jointly optimizes sequential index-list scheduling and random score accesses in threshold algorithms, unlike prior work treating them separately. Knapsack-based ordering, random-access cost models, and probabilistic estimators substantially accelerate top-k processing. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Holger Bast (Max Planck Institute)
- 2. Debapriyo Majumdar (Max Planck Institute)
- 3. Ralf Schenkel (Max Planck Institute)
- 4. Martin Theobald (Max Planck Institute)
- 5. Gerhard Weikum (Max Planck Institute)
BibTeX Citation
@article{bast_vldb06,
title = {{IO-Top-k: Index-access Optimized Top-k Query Processing}},
author = {Bast, Holger and Majumdar, Debapriyo and Schenkel, Ralf and Theobald, Martin and Weikum, Gerhard},
journal = {PVLDB},
series = {{VLDB} '06},
pages = {475--486},
year = {2006}
}
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