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AS-Parser: Log Parsing Based on Adaptive Segmentation

Summary: AS-Parser presents adaptive hierarchical segmentation, yielding a tree-based representation that captures log structure beyond fixed delimiters. It auto-discovers delimiters and offers three improvements, achieving 0.943 accuracy on 14/16 benchmarks. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6796
Venue
SIGMOD
Year
2023
Pagerank
6.6599757e-05
Overall Rank
4,502 | 69.12%
DOI
10.1145/3626719

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{chen_sigmod23,
        title = {{AS-Parser: Log Parsing Based on Adaptive Segmentation}},
        author = {Chen, Xiaolei and Wang, Peng and Chen, Jia and Wang, Wei},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3626719},
        url = {https://dl.acm.org/doi/10.1145/3626719},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
5,713 PreLog: A Pre-trained Model for Log Analytics 2024 SIGMOD 6.1123632e-05
7,499 Adaptive and Efficient Log Parsing as a Cloud Service 2025 SIGMOD 5.6029996e-05
10,956 CoLA: Model Collaboration for Log-based Anomaly Detection 2025 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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