Answering Top-k Queries Using Views
Summary: Explores space-performance tradeoffs in top-k query answering via materialized views. Presents a view-fusion algorithm for combining multiple views under monotone aggregations and a view-selection framework to identify the most promising views, with formalization and extensive experiments. (summarized by gpt-5-nano on Feb 09 2026)
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 82 | Answering Queries Using Views (Extended Abstract) | 1995 | PODS | 0.00054402763 |
| 7,692 | Processing Top-k Join Queries | 2010 | VLDB | 4.6761366e-05 |
| 805 | Evaluating Top-k Selection Queries | 1999 | VLDB | 0.00016437265 |
| 7,963 | Efficient Top-K Processing Over Query-Dependent Functions | 2008 | VLDB | 4.613363e-05 |
| 584 | Answering Queries with Aggregation Using Views | 1996 | VLDB | 0.0001971526 |
| 12,135 | Answering Top-k Queries Over a Mixture of Attractive and Repulsive Dimensions | 2012 | VLDB | 4.1945683e-05 |
| 3,665 | Ad-hoc Top-k Query Answering for Data Streams | 2007 | VLDB | 6.8633354e-05 |
| 12,111 | Optimal Top-k Generation of Attribute Combinations based on Ranked Lists | 2012 | SIGMOD | 4.1945683e-05 |
| 1,784 | Continuous Monitoring of Top-k Queries over Sliding Windows | 2006 | SIGMOD | 0.00010567363 |
| 7,276 | Efficient and Generic Evaluation of Ranked Queries | 2011 | SIGMOD | 4.7798595e-05 |