DBScholar

Back to papers

A Demonstration of the Exathlon Benchmarking Platform for Explainable Anomaly Detection

Summary: Exathlon is a benchmarking platform for explainable anomaly detection on high-dimensional time series, pairing a curated anomaly dataset with novel evaluation methodology. A demo shows its end-to-end pipeline for detecting anomalies and discovering explanations. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
12672
Venue
VLDB
Year
2021
Pagerank
5.3096515e-05
Overall Rank
9,170 | 37.09%
DOI
10.14778/3476311.3476355

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{jacob_vldb21,
        title = {{A Demonstration of the Exathlon Benchmarking Platform for Explainable Anomaly Detection}},
        author = {Jacob, Vincent and Song, Fei and Stiegler, Arnaud and Rad, Bijan and Diao, Yanlei and Tatbul, Nesime},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {12},
        pages = {2827--2830},
        doi = {10.14778/3476311.3476355},
        url = {https://doi.org/10.14778/3476311.3476355},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
5,023 DBPA: A Benchmark for Transactional Database Performance Anomalies 2023 SIGMOD 6.3979497e-05
8,230 TOD: GPU-accelerated Outlier Detection via Tensor Operations 2023 VLDB 5.4619615e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers