ADOps: An Anomaly Detection Pipeline in Structured Logs
Summary: Presents ADOps, a modular anomaly-detection pipeline for structured logs that decomposes acquisition, processing, training, and deployment to enable config-driven, no-code creation of simple detectors. Also accelerates development and integration of complex models. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Xintong Song (NetEase Fuxi Artificial Intelligence Lab)
- 2. Yusen Zhu (NetEase Fuxi Artificial Intelligence Lab)
- 3. Jianfei Wu (NetEase Fuxi Artificial Intelligence Lab)
- 4. Bai Liu (NetEase Fuxi Artificial Intelligence Lab)
- 5. Hongkang Wei (NetEase Fuxi Artificial Intelligence Lab)
BibTeX Citation
@article{song_vldb23,
title = {{ADOps: An Anomaly Detection Pipeline in Structured Logs}},
author = {Song, Xintong and Zhu, Yusen and Wu, Jianfei and Liu, Bai and Wei, Hongkang},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {12},
pages = {4050--4053},
doi = {10.14778/3611540.3611618},
url = {https://doi.org/10.14778/3611540.3611618},
year = {2023}
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Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 330 | Impala: A Modern, Open-Source SQL Engine for Hadoop | 2015 | CIDR | 0.0002104801 |
| 3,555 | Apache Hive: From MapReduce to Enterprise-grade Big Data Warehousing | 2019 | SIGMOD | 7.3115321e-05 |
| 4,582 | A Demonstration of AutoOD: A Self-Tuning Anomaly Detection System | 2022 | VLDB | 6.6193306e-05 |
| 6,105 | Optimizing In-memory Database Engine for AI-powered On-line Decision Augmentation Using Persistent Memory | 2021 | VLDB | 5.9743349e-05 |
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