Durable Top-k Queries on Temporal Data
Summary: Introduces durable top-k queries over temporal histories: objects ranked in the top k for a specified fraction of timestamps. Provides exact fixed-k algorithms and adaptive approximate methods for arbitrary k, exploiting workload/data properties while bounding index cost. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Junyang Gao (Duke University)
- 2. Pankaj K. Agarwal (Duke University)
- 3. Jun Yang (Duke University)
BibTeX Citation
@article{gao_vldb18,
title = {{Durable Top-k Queries on Temporal Data}},
author = {Gao, Junyang and Agarwal, Pankaj K. and Yang, Jun},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {13},
pages = {2223--2235},
doi = {10.14778/3275366.3275371},
url = {https://doi.org/10.14778/3275366.3275371},
year = {2018}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,761 | Efficiently Answering Durability Prediction Queries | 2021 | SIGMOD | 5.7822533e-05 |
| 8,691 | On Reporting Durable Patterns in Temporal Proximity Graphs | 2024 | PODS | 5.3839057e-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 | 0.0010828372 |
| 1,100 | Range Queries in OLAP Data Cubes | 1997 | SIGMOD | 0.00012169143 |
| 4,765 | Durable Top-k Search in Document Archives | 2010 | SIGMOD | 6.5186944e-05 |
| 7,934 | Ranking Large Temporal Data | 2012 | VLDB | 5.5181056e-05 |
| 9,018 | Finding Diverse, High-Value Representatives on a Surface of Answers | 2017 | VLDB | 5.3309706e-05 |
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