Ad-hoc Top-k Query Answering for Data Streams
Summary: Geometric arrangement representation enables ad-hoc top-k queries over data streams, not limited to pre-specified targets. Incremental maintenance plus pruning yields a main-memory index for streaming updates and fast ad-hoc evaluation, with experiments. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Gautam Das
- 2. Dimitrios Gunopulos
- 3. Nick Koudas
- 4. Nikos Sarkas
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 802 | Evaluating Top-k Selection Queries | 1999 | VLDB | 0.00016440813 |
| 2,967 | Processing a Large Number of Continuous Preference Top-k Queries | 2012 | SIGMOD | 7.7975455e-05 |
| 12,119 | Optimal Top-k Generation of Attribute Combinations based on Ranked Lists | 2012 | SIGMOD | 4.1905499e-05 |
| 2,936 | Answering Top-k Queries Using Views | 2006 | VLDB | 7.8579393e-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 |
| 3,809 | Supporting Ad-hoc Ranking Aggregates | 2006 | SIGMOD | 6.7413981e-05 |
| 1,773 | Continuous Monitoring of Top-k Queries over Sliding Windows | 2006 | SIGMOD | 0.00010600438 |
| 4,328 | Sliding-Window Top-k Queries on Uncertain Streams | 2008 | VLDB | 6.2765138e-05 |