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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)

Paper ID
12869
Venue
VLDB
Year
2022
Pagerank
6.0852762e-05
Overall Rank
4,553 | 68.36%
DOI
10.14778/3554821.3554880

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

Rank Citing Paper Year Venue Pagerank
4,455 AutoOD: Automatic Outlier Detection 2023 SIGMOD 6.1644904e-05
9,871 Substructure-aware Log Anomaly Detection 2025 VLDB 4.2626861e-05
9,983 Towards Scalable Visual Data Wrangling via Direct Manipulation 2026 CIDR 4.1905499e-05
10,834 EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection 2025 VLDB 4.1905499e-05
11,011 MetaStore: Analyzing Deep Learning Meta-Data at Scale 2024 VLDB 4.1905499e-05
11,293 ADOps: An Anomaly Detection Pipeline in Structured Logs 2023 VLDB 4.1905499e-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.

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