Fast Anomaly Detection on Multiple Multi-dimensional Data Streams



Publication Details

Sun, H., He, Q., Liao, K., Sellis, T., Guo, L., Zhang, X., Shen, J. & Chen, F. (2019). Fast Anomaly Detection on Multiple Multi-dimensional Data Streams. IEEE International Conference on Big Data (Big Data 2019) (pp. 1218-1223). United States: IEEE.


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-theart approaches while maintaining competitive detection accuracy.

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