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 |
|---|---|---|---|---|
| 4,103 | AutoOD: Automatic Outlier Detection | 2023 | SIGMOD | 6.8066074e-05 |
| 10,208 | Substructure-aware Log Anomaly Detection | 2025 | VLDB | 5.0596605e-05 |
| 10,352 | Towards Scalable Visual Data Wrangling via Direct Manipulation | 2026 | CIDR | 4.9793485e-05 |
| 11,419 | EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection | 2025 | VLDB | 4.9793485e-05 |
| 11,556 | MetaStore: Analyzing Deep Learning Meta-Data at Scale | 2024 | VLDB | 4.9793485e-05 |
| 11,799 | ADOps: An Anomaly Detection Pipeline in Structured Logs | 2023 | VLDB | 4.9793485e-05 |
Previous
Page 1 / 1
Next
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 | 0.00029202746 |
| 583 | Efficient Algorithms for Mining Outliers from Large Data Sets | 2000 | SIGMOD | 0.00015960125 |
| 695 | Algorithms for Mining Distance-Based Outliers in Large Datasets | 1998 | VLDB | 0.00014702685 |
| 975 | Democratizing Data Science through Interactive Curation of ML Pipelines | 2019 | SIGMOD | 0.00012750518 |
| 1,833 | MacroBase: Prioritizing Attention in Fast Data | 2017 | SIGMOD | 9.5405247e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 11,419 | EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection | 2025 | VLDB |
| 2 | 3,449 | Robust and Transferable Log-based Anomaly Detection | 2023 | SIGMOD |
| 3 | 9,725 | OIE: An Interpretable System for Outlier Explanation and Summarization | 2025 | SIGMOD |
| 4 | 9,564 | Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains | 2025 | VLDB |
| 5 | 6,268 | An Experimental Evaluation of Anomaly Detection in Time Series | 2024 | VLDB |
| 6 | 9,482 | TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection | 2025 | VLDB |
| 7 | 11,799 | ADOps: An Anomaly Detection Pipeline in Structured Logs | 2023 | VLDB |
| 8 | 9,488 | Time-Series Anomaly Detection: Overview and New Trends | 2024 | VLDB |
| 9 | 6,073 | AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data | 2024 | VLDB |
| 10 | 4,103 | AutoOD: Automatic Outlier Detection | 2023 | SIGMOD |