Continuous Query for Top-K Maximal Sum Intervals over Streaming Data
Summary: Introduces a partition-based method for continuous top-k maximal-sum intervals over sliding-window streams. Partitions contain every interval wholly, enabling safe partition pruning, parallel processing, and efficient incremental maintenance without costly index restructuring. (summarized by gpt-5.6-luna on Aug 17 2026)
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Authors
- 1. Zhongshuai Zhang (Beijing Institute of Technology)
- 2. Xiaochun Yang (Northeastern University)
- 3. Baihua Zheng (Singapore Management University)
- 4. Rui Zhu (Shenyang Aerospace University)
- 5. Haomin Li (Northeastern University)
- 6. Bin Wang (Northeastern University)
BibTeX Citation
@article{zhang_vldb26,
title = {{Continuous Query for Top-K Maximal Sum Intervals over Streaming Data}},
author = {Zhang, Zhongshuai and Yang, Xiaochun and Zheng, Baihua and Zhu, Rui and Li, Haomin and Wang, Bin},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {9},
pages = {2289--2302},
doi = {10.14778/3819518.3819551},
url = {https://doi.org/10.14778/3819518.3819551},
year = {2026}
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,439 | Continuous Monitoring of Top-k Queries over Sliding Windows | 2006 | SIGMOD | 0.00010642846 |
| 1,480 | Distance-based Outlier Detection in Data Streams | 2016 | VLDB | 0.00010540994 |
| 2,547 | NETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing | 2019 | VLDB | 8.3160201e-05 |
| 2,907 | Real-Time Distance-Based Outlier Detection in Data Streams | 2021 | VLDB | 7.8769563e-05 |
| 3,326 | Sliding-Window Top-k Queries on Uncertain Streams | 2008 | VLDB | 7.4227632e-05 |
| 8,700 | Upsortable: Programming Top-K Queries Over Data Streams | 2017 | VLDB | 5.2905577e-05 |
| 11,722 | Closest Pairs Search Over Data Stream | 2023 | SIGMOD | 4.9793485e-05 |
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