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Detecting Abnormal Congregation Through the Analysis of Massive Spatio-Temporal Data

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Web and Big Data (APWeb-WAIM 2020)

Abstract

The pervasiveness of location-acquisition technologies leads to large amounts of spatio-temporal data, which brings researchers opportunities to discover interesting group patterns like congregation. Typically, a congregation is formed by a certain number of individuals within an area during a period of time. Previous work focused on discovering various congregations based on real-life scenarios to help in monitoring unusual group activities. However, most existing research didn’t further analyze these results due to the consideration that the congregation is an unusual event already. In this article, firstly, we propose a group pattern to capture a variety of congregations from trajectory data. Secondly, congregations are separated into unexpected congregations and periodic congregations by extracting spatio-temporal features from historical trajectories. Thirdly, we further investigate the intensity of periodic congregation and combine environmental factors to dynamically identify anomalies within it, together with previously obtained unexpected congregations to form abnormal congregations. Moreover, incremental update techniques are utilized to detect abnormal congregations over massive-scale trajectory streams online, which means it can immediately respond to the updated trajectories. Finally, based on real cellular network dataset and real taxi trajectory dataset, our approach is evaluated through extensive experiments which demonstrate its effectiveness.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant No. U1736218 and the Beijing Municipal Science & Technology Commission under Grant No. Z191100007119005.

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Correspondence to Yongzheng Zhang .

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Chen, T., Zhang, Y., Tuo, Y., Wang, W. (2020). Detecting Abnormal Congregation Through the Analysis of Massive Spatio-Temporal Data. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12318. Springer, Cham. https://doi.org/10.1007/978-3-030-60290-1_30

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  • DOI: https://doi.org/10.1007/978-3-030-60290-1_30

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  • Online ISBN: 978-3-030-60290-1

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