OIE: An Interpretable System for Outlier Explanation and Summarization
Summary: OIE is an interpretable system for outlier explanation and summarization, delivering fine-grained, rule-based insights instead of opaque flags. It uses decision trees to generate concise rules, with dynamic data partitioning and a boundary stabilizer to scale to high-dimensional data and subspace-specific outlier causes. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jingzhe Xu (Beijing Institute of Technology)
- 2. Yuhao Deng (Beijing Institute of Technology)
- 3. Chengliang Chai (Beijing Institute of Technology)
- 4. Zequn Li (Beijing Institute of Technology)
- 5. Yuping Wang (Beijing Institute of Technology)
- 6. Lei Cao (University of Arizona)
BibTeX Citation
@inproceedings{xu_sigmod25,
title = {{OIE: An Interpretable System for Outlier Explanation and Summarization}},
author = {Xu, Jingzhe and Deng, Yuhao and Chai, Chengliang and Li, Zequn and Wang, Yuping and Cao, Lei},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3722212.3725120},
url = {https://dl.acm.org/doi/10.1145/3722212.3725120},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,131 | Towards Scalable Visual Data Wrangling via Direct Manipulation | 2026 | CIDR | 5.093636e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 191 | Scorpion: Explaining Away Outliers in Aggregate Queries | 2013 | VLDB | 0.00026096009 |
| 8,802 | Outlier Summarization via Human Interpretable Rules | 2024 | VLDB | 5.3685765e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,324 | Outliers: The Good, the Bad and the Ugly | 2026 | SIGMOD |
| 2 | 2,340 | Online Outlier Detection in Sensor Data Using Non-Parametric Models | 2006 | VLDB |
| 3 | 4,582 | A Demonstration of AutoOD: A Self-Tuning Anomaly Detection System | 2022 | VLDB |
| 4 | 4,209 | Outlier Detection for High Dimensional Data | 2001 | SIGMOD |
| 5 | 9,952 | Distance-Based Outlier Detection: Consolidation and Renewed Bearing | 2010 | VLDB |
| 6 | 3,958 | Continuous Outlier Detection in Data Streams: An Extensible Framework and State-Of-The-Art Algorithms | 2013 | SIGMOD |
| 7 | 4,062 | AutoOD: Automatic Outlier Detection | 2023 | SIGMOD |
| 8 | 3,031 | Interactive Outlier Exploration in Big Data Streams | 2014 | VLDB |
| 9 | 6,899 | Human-in-the-loop Outlier Detection | 2020 | SIGMOD |
| 10 | 8,802 | Outlier Summarization via Human Interpretable Rules | 2024 | VLDB |