@inproceedings{cb7870c38cc543d4804322d68f31a074,
title = "Fast anomaly detection in multiple multi-dimensional data streams",
abstract = "Multiple multi-dimensional data streams are ubiquitous in the modern world, such as IoT applications, GIS applications and social networks. Detecting anomalies in such data streams in real-time is an important and challenging task. It is able to provide valuable information from data and then assists decision-making. However, exiting approaches for anomaly detection in multi-dimensional data streams have not properly considered the correlations among multiple multi-dimensional streams. Moreover, for multi-dimensional streaming data, online detection speed is often an important concern. In this paper, we propose a fast yet effective anomaly detection approach in multiple multi-dimensional data streams. This is based on a combination of ideas, i.e., stream pre-processing, locality sensitive hashing and dynamic isolation forest. Experiments on real datasets demonstrate that our approach achieves a magnitude increase in its efficiency compared with state-of-the-art approaches while maintaining competitive detection accuracy.",
author = "Hongyu Sun and Qiang He and Kewen Liao and Timos Sellis and Longkun Guo and Xuyun Zhang and Jun Shen and Feifei Chen",
year = "2019",
doi = "10.1109/BigData47090.2019.9006354",
language = "English",
isbn = "9781728108599",
series = "Ieee International Conference On Big Data",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "1218--1223",
editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Hu, \{Xiaohua Tony\} and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, \{Yanfang Fanny\}",
booktitle = "2019 IEEE International Conference on Big Data",
address = "United States",
note = "2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",
}