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 |
|---|---|---|---|---|
| 11,833 | Efficient Top-k Indexing via General Reductions | 2016 | PODS | 4.1905499e-05 |
| 3,665 | Ad-hoc Top-k Query Answering for Data Streams | 2007 | VLDB | 6.8631567e-05 |
| 2,967 | Processing a Large Number of Continuous Preference Top-k Queries | 2012 | SIGMOD | 7.7975455e-05 |
| 3,447 | Towards Robust Indexing for Ranked Queries | 2006 | VLDB | 7.082382e-05 |
| 7,133 | Anytime Measures for Top-k Algorithms | 2007 | VLDB | 4.8175888e-05 |
| 3,911 | Progressive and Selective Merge: Computing Top-K with Ad-hoc Ranking Functions | 2007 | SIGMOD | 6.6343292e-05 |
| 3,046 | An Efficient and Versatile Query Engine for TopX Search | 2005 | VLDB | 7.6586679e-05 |
| 7,966 | Efficient Top-K Processing Over Query-Dependent Functions | 2008 | VLDB | 4.6089395e-05 |
| 12,119 | Optimal Top-k Generation of Attribute Combinations based on Ranked Lists | 2012 | SIGMOD | 4.1905499e-05 |
| 7,273 | Efficient and Generic Evaluation of Ranked Queries | 2011 | SIGMOD | 4.775366e-05 |