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)
Incoming Non-self Citations Over Time
Authors
- 1. Gautam Das (University of Texas)
- 2. Dimitrios Gunopulos (University of California Riverside)
- 3. Dimitris Tsirogiannis (University of Toronto)
- 4. Nick Koudas (University of Toronto)
BibTeX Citation
@article{das_vldb06,
title = {{Answering Top-k Queries Using Views}},
author = {Das, Gautam and Gunopulos, Dimitrios and Tsirogiannis, Dimitris and Koudas, Nick},
journal = {PVLDB},
series = {{VLDB} '06},
pages = {451--462},
year = {2006}
}
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|---|---|---|---|---|
| 1 | 69 | Answering Queries Using Views (Extended Abstract) | 1995 | PODS |
| 2 | 635 | Evaluating Top-k Selection Queries | 1999 | VLDB |
| 3 | 7,550 | Processing Top-k Join Queries | 2010 | VLDB |
| 4 | 554 | Answering Queries with Aggregation Using Views | 1996 | VLDB |
| 5 | 8,281 | Efficient Top-K Processing Over Query-Dependent Functions | 2008 | VLDB |
| 6 | 3,317 | Ad-hoc Top-k Query Answering for Data Streams | 2007 | VLDB |
| 7 | 12,331 | Answering Top-k Queries Over a Mixture of Attractive and Repulsive Dimensions | 2012 | VLDB |
| 8 | 12,308 | Optimal Top-k Generation of Attribute Combinations based on Ranked Lists | 2012 | SIGMOD |
| 9 | 1,513 | Continuous Monitoring of Top-k Queries over Sliding Windows | 2006 | SIGMOD |
| 10 | 7,299 | Efficient and Generic Evaluation of Ranked Queries | 2011 | SIGMOD |