IO-Top-k: Index-access Optimized Top-k Query Processing
Summary: IO-Top-k: scheduling for top-k over index lists, optimizing sequential access order and selective random lookups. Knapsack-based sequencing, random-access model, and probabilistic estimators for scores and selectivities yield large gains over prior methods. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Holger Bast
- 2. Debapriyo Majumdar
- 3. Ralf Schenkel
- 4. Martin Theobald
- 5. Gerhard Weikum
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,825 | Efficient Top-k Indexing via General Reductions | 2016 | PODS | 4.1945683e-05 |
| 3,665 | Ad-hoc Top-k Query Answering for Data Streams | 2007 | VLDB | 6.8633354e-05 |
| 2,976 | Processing a Large Number of Continuous Preference Top-k Queries | 2012 | SIGMOD | 7.789303e-05 |
| 3,463 | Towards Robust Indexing for Ranked Queries | 2006 | VLDB | 7.069675e-05 |
| 7,135 | Anytime Measures for Top-k Algorithms | 2007 | VLDB | 4.8221884e-05 |
| 3,908 | Progressive and Selective Merge: Computing Top-K with Ad-hoc Ranking Functions | 2007 | SIGMOD | 6.6392878e-05 |
| 3,044 | An Efficient and Versatile Query Engine for TopX Search | 2005 | VLDB | 7.6640252e-05 |
| 7,963 | Efficient Top-K Processing Over Query-Dependent Functions | 2008 | VLDB | 4.613363e-05 |
| 12,111 | Optimal Top-k Generation of Attribute Combinations based on Ranked Lists | 2012 | SIGMOD | 4.1945683e-05 |
| 7,276 | Efficient and Generic Evaluation of Ranked Queries | 2011 | SIGMOD | 4.7798595e-05 |