A Demonstration of the Exathlon Benchmarking Platform for Explainable Anomaly Detection

被引：0

作者：

Jacob, Vincent ^{[1
]}

Song, Fei ^{[1
]}

Stiegler, Arnaud ^{[1
]}

Rad, Bijan ^{[1
]}

Diao, Yanlei ^{[1
]}

Tatbul, Nesime ^{[2
,3
]}

机构：

[1] Ecole Polytech, Palaiseau, France

[2] Intel Labs, New York, NY USA

[3] MIT, Cambridge, MA 02139 USA

来源：

PROCEEDINGS OF THE VLDB ENDOWMENT | 2021年 / 14卷 / 12期

基金：

欧洲研究理事会;

关键词：

D O I：

10.14778/3476311.3476355

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this demo, we introduce Exathlon - a new benchmarking platform for explainable anomaly detection over high-dimensional time series. We designed Exathlon to support data scientists and researchers in developing and evaluating learned models and algorithms for detecting anomalous patterns as well as discovering their explanations. This demo will showcase Exathlon's curated anomaly dataset, novel benchmarking methodology, and end-to-end data science pipeline in action via example usage scenarios.

引用

页码：2827 / 2830

页数：4

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