Continuous Monitoring of Top-k Queries over Sliding Windows
Summary: Continuous monitoring of top-k on a sliding window, with an in-memory grid index confining processing to result-relevant regions. Two techniques: recompute at top-k expiry; precompute changes for faster online answers; extend to other queries. (summarized by gpt-5-nano on Feb 09 2026)
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| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,934 | Static Optimization of Conjunctive Queries with Sliding Windows Over Infinite Streams | 2004 | SIGMOD | 5.815192e-05 |
| 7,280 | Update-Pattern-Aware Modeling and Processing of Continuous Queries | 2005 | SIGMOD | 4.7718173e-05 |
| 7,966 | Efficient Top-K Processing Over Query-Dependent Functions | 2008 | VLDB | 4.6089395e-05 |
| 7,273 | Efficient and Generic Evaluation of Ranked Queries | 2011 | SIGMOD | 4.775366e-05 |
| 7,660 | Processing Top-k Join Queries | 2010 | VLDB | 4.6814547e-05 |
| 743 | Distributed Top-K Monitoring | 2003 | SIGMOD | 0.00017318557 |
| 2,936 | Answering Top-k Queries Using Views | 2006 | VLDB | 7.8579393e-05 |
| 2,967 | Processing a Large Number of Continuous Preference Top-k Queries | 2012 | SIGMOD | 7.7975455e-05 |
| 3,665 | Ad-hoc Top-k Query Answering for Data Streams | 2007 | VLDB | 6.8631567e-05 |
| 4,328 | Sliding-Window Top-k Queries on Uncertain Streams | 2008 | VLDB | 6.2765138e-05 |