Durable Top-k Queries on Temporal Data
Summary: Durable top-k queries on temporal data: identify objects in the top-k for a fraction of time in an interval. Exact fixed-k algorithms; approximate methods for any k exploit workload to bound index cost and improve accuracy, with empirical gains. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Junyang Gao
- 2. Pankaj K. Agarwal
- 3. Jun Yang
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,654 | Efficiently Answering Durability Prediction Queries | 2021 | SIGMOD | 5.8715116e-05 |
| 8,707 | On Reporting Durable Patterns in Temporal Proximity Graphs | 2024 | PODS | 5.4436932e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5 | Optimal Aggregation Algorithms for Middleware [Extended Abstract] | 2001 | PODS | 0.0010926229 |
| 1,089 | Range Queries in OLAP Data Cubes | 1997 | SIGMOD | 0.00012324351 |
| 4,698 | Durable Top-k Search in Document Archives | 2010 | SIGMOD | 6.6196426e-05 |
| 7,819 | Ranking Large Temporal Data | 2012 | VLDB | 5.6035684e-05 |
| 8,892 | Finding Diverse, High-Value Representatives on a Surface of Answers | 2017 | VLDB | 5.4135362e-05 |
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