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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)

Paper ID
7231
Venue
SIGMOD
Year
2025
Pagerank
5.2528121e-05
Overall Rank
9,543 | 34.53%
DOI
10.1145/3722212.3725120

Incoming Non-self Citations Over Time

Authors

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
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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
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