ABFlow: Alert Bursting Flow Query in Streaming Temporal Flow Networks
Summary: Introduces ABFlow, a new streaming query for temporal flow networks that finds the most bursty S→T flow, i.e., max flow/duration, to support alert/anomaly detection in applications like fraud. Core technical novelty is a suffix-flow reformulation plus incremental/recursive streaming optimizations (SuffixFlowstr), delivering large speedups over baselines. (summarized by gpt-5-mini on Apr 11 2026)
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Authors
- 1. Yunxiang Zhao (Hong Kong Baptist University)
- 2. Lyu Xu (Hong Kong Polytechnic University)
- 3. Jiaxin Jiang (National University of Singapore)
- 4. Byron Choi (Hong Kong Baptist University)
- 5. Jianliang Xu (Hong Kong Baptist University)
- 6. Bingsheng He (National University of Singapore)
BibTeX Citation
@inproceedings{zhao_sigmod26,
title = {{ABFlow: Alert Bursting Flow Query in Streaming Temporal Flow Networks}},
author = {Zhao, Yunxiang and Xu, Lyu and Jiang, Jiaxin and Choi, Byron and Xu, Jianliang and He, Bingsheng},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3786619},
url = {https://dl.acm.org/doi/10.1145/3786619},
year = {2026}
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