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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)

Paper ID
6511
Venue
SIGMOD
Year
2022
Pagerank
5.3623113e-05
Overall Rank
8,824 | 39.46%
DOI
10.1145/3514221.3526136

Incoming Non-self Citations Over Time

Authors

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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