DLACEP: A Deep-Learning Based Framework for Approximate Complex Event Processing
Summary: DLACEP fuses deep learning with CEP to prune candidate event sets and approximate pattern matches in streams. Empirical results show throughput gains up to 1,000× with minor loss in matches, enabling a scalable DL-CEP framework for pattern detection. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Adar Amir (Technion)
- 2. Ilya Kolchinsky (Technion)
- 3. Assaf Schuster (Technion)
BibTeX Citation
@inproceedings{amir_sigmod22,
title = {{DLACEP: A Deep-Learning Based Framework for Approximate Complex Event Processing}},
author = {Amir, Adar and Kolchinsky, Ilya and Schuster, Assaf},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3526136},
url = {https://dl.acm.org/doi/10.1145/3514221.3526136},
year = {2022}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,779 | SuSe: Summary Selection for Regular Expression Subsequence Aggregation over Streams | 2025 | SIGMOD | 5.093636e-05 |
| 10,795 | Scalable Complex Event Processing on Video Streams | 2025 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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