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EXAD: A System for Explainable Anomaly Detection on Big Data Traces

Abstract : Big Data systems are producing huge amounts of data in real-time. Finding anomalies in these systems is becoming increasingly important, since it can help to reduce the number of failures, and improve the mean time of recovery. In this work, we present EXAD, a new prototype system for explainable anomaly detection, in particular for detecting and explaining anomalies in time-series data obtained from traces of Apache Spark jobs. Apache Spark has become the most popular software tool for processing Big Data. The new system contains the most well-known approaches to anomaly detection, and a novel generator of artificial traces, that can help the user to understand the different performances of the different methodologies. In this demo, we will show how this new framework works, and how users can benefit of detecting anomalies in an efficient and fast way when dealing with traces of jobs of Big Data systems.
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Contributor : Fei Song <>
Submitted on : Wednesday, August 7, 2019 - 11:40:09 AM
Last modification on : Friday, April 30, 2021 - 9:55:13 AM
Long-term archiving on: : Thursday, January 9, 2020 - 2:23:50 AM


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  • HAL Id : hal-02264598, version 1


Fei Song, Arnaud Stiegler, Yanlei Diao, Jesse Read, Albert Bifet. EXAD: A System for Explainable Anomaly Detection on Big Data Traces. ICDMW 2018 - IEEE International Conference on Data Mining Workshops, Nov 2018, Singapore, Singapore. ⟨hal-02264598⟩



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