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Anomaly behaviour detection based on the meta‐Morisita index for large scale spatio‐temporal data set

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posted on 2023-08-05, 11:36 authored by Zhao Yang, Nathalie JapkowiczNathalie Japkowicz

In this paper, we propose a framework for processing and analysing large-scale spatio-temporal data that uses a battery of machine learning methods based on a meta-data representation of point patterns. Existing spatio-temporal analysis methods do not include a specific mechanism for analysing meta-data (point pattern information). In this work, we extend a spatial point pattern analysis method (the Morisita index) with meta-data analysis, which includes anomaly behaviour detection and unsupervised learning to support spatio-temporal data analysis and demonstrate its practical use. The resulting framework is robust and has the capability to detect anomalies among large-scale spatio-temporal data using meta-data based on point pattern analysis. It returns visualized reports to end users.

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American University

Notes

Published in: Journal of Big Data, volume 5, Article number: 23 (2018).

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http://hdl.handle.net/1961/auislandora:83386

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