ORDEN: Outlier Region Detection and Exploration in Sensor Networks

Summary: ORDEN detects real-time degree-based outliers and builds heterogeneous regions to delineate anomaly extent. It enables interactive exploration of region boundaries, thresholds, and inter-outlier correlations for context-aware analysis. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 4272
Venue: SIGMOD
Year: 2009
Pagerank: -
Overall Rank: 13,738 | 5.75%
DOI: 10.1145/1559845.1559985

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Authors

1. Conny Franke (University of California Davis)
2. Michael Gertz (University of Heidelberg)

BibTeX Citation

@inproceedings{franke_sigmod09,
        title = {{ORDEN: Outlier Region Detection and Exploration in Sensor Networks}},
        author = {Franke, Conny and Gertz, Michael},
        series = {{SIGMOD} '09},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1559845.1559985},
        url = {https://dl.acm.org/doi/10.1145/1559845.1559985},
        year = {2009}
}

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