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
- 2. Yusen Zhu
- 3. Jianfei Wu
- 4. Bai Liu
- 5. Hongkang Wei
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 476 | Impala: A Modern, Open-Source SQL Engine for Hadoop | 2015 | CIDR | 0.00022226941 |
| 3,973 | Apache Hive: From MapReduce to Enterprise-grade Big Data Warehousing | 2019 | SIGMOD | 6.5758017e-05 |
| 4,554 | A Demonstration of AutoOD: A Self-Tuning Anomaly Detection System | 2022 | VLDB | 6.0911296e-05 |
| 6,247 | Optimizing In-memory Database Engine for AI-powered On-line Decision Augmentation Using Persistent Memory | 2021 | VLDB | 5.1389201e-05 |
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