A Demonstration of AutoOD: A Self-Tuning Anomaly Detection System
Summary: Demo of AutoOD, an unsupervised self-tuning anomaly detector that eliminates manual model selection. AutoOD matches supervised performance without labels and provides a visual interface to inspect its self-tuning choices and data patterns. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Dennis Hofmann (Worcester Polytechnic Institute)
- 2. Peter VanNostrand (Worcester Polytechnic Institute)
- 3. Huayi Zhang (Worcester Polytechnic Institute)
- 4. Yizhou Yan (Meta)
- 5. Lei Cao (Massachusetts Institute of Technology)
- 6. Samuel Madden (Massachusetts Institute of Technology)
- 7. Elke Rundensteiner (Worcester Polytechnic Institute)
BibTeX Citation
@article{hofmann_vldb22,
title = {{A Demonstration of AutoOD: A Self-Tuning Anomaly Detection System}},
author = {Hofmann, Dennis and VanNostrand, Peter and Zhang, Huayi and Yan, Yizhou and Cao, Lei and Madden, Samuel and Rundensteiner, Elke},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {12},
pages = {3706--3709},
doi = {10.14778/3554821.3554880},
url = {https://doi.org/10.14778/3554821.3554880},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,062 | AutoOD: Automatic Outlier Detection | 2023 | SIGMOD | 6.9309994e-05 |
| 10,019 | Substructure-aware Log Anomaly Detection | 2025 | VLDB | 5.1757914e-05 |
| 10,131 | Towards Scalable Visual Data Wrangling via Direct Manipulation | 2026 | CIDR | 5.093636e-05 |
| 11,056 | EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection | 2025 | VLDB | 5.093636e-05 |
| 11,219 | MetaStore: Analyzing Deep Learning Meta-Data at Scale | 2024 | VLDB | 5.093636e-05 |
| 11,490 | ADOps: An Anomaly Detection Pipeline in Structured Logs | 2023 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 142 | LOF: Identifying Density-Based Local Outliers | 2000 | SIGMOD | 0.0002962566 |
| 578 | Efficient Algorithms for Mining Outliers from Large Data Sets | 2000 | SIGMOD | 0.00016221871 |
| 693 | Algorithms for Mining Distance-Based Outliers in Large Datasets | 1998 | VLDB | 0.00014918477 |
| 1,004 | Democratizing Data Science through Interactive Curation of ML Pipelines | 2019 | SIGMOD | 0.00012701932 |
| 1,792 | MacroBase: Prioritizing Attention in Fast Data | 2017 | SIGMOD | 9.7436856e-05 |
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