@inproceedings{d11e75f9ed7841a0b72545138888d5fa,
title = "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.",
keywords = "Spark, anomaly detection, machine learning",
author = "Fei Song and Yanlei Diao and Jesse Read and Arnaud Stiegler and Albert Bifet",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018 ; Conference date: 17-11-2018 Through 20-11-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/ICDMW.2018.00204",
language = "English",
series = "IEEE International Conference on Data Mining Workshops, ICDMW",
publisher = "IEEE Computer Society",
pages = "1435--1440",
editor = "Hanghang Tong and Zhenhui Li and Feida Zhu and Jeffrey Yu",
booktitle = "Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018",
}