Andreas Lohrer
ABSTRACT
Group anomaly detection in terms of detecting and predicting abnormal behaviour from entities as a group rather than as an individual, addresses a variety of challenges in spatio-temporal environments like e.g. traffic and transportation systems, smart cities, geoinformation systems, etc. They provide information about a commonly large number of individual entities. Examples for such entities would be airplanes and drones, vehicles, ships but also people, remote sensors and any other information source in interaction with the environment. However, as point anomaly detection is quite common for revealing the abnormal behaviour of individual entities, the collective behaviour of the individuals as a group remains completely uncovered. For example potential for traffic flow optimizations or increased local traffic guideline violations cannot be detected by one single drive but by considering the behavior of a group of vehicle drives in this area. With this work-in-progress we elaborate the potential of group anomaly detection algorithms for spatio-temporal collective behaviour scenarios in smart cities. We describe the group anomaly detection problem in the context of urban planning and demonstrate its effectiveness on a public real-world data set for urban rental bike rides and stations in and around Munich revealing abnormal groups of rides, which allows to optimize the rental bike accessibility to the population and with that to contribute to a sustainable environment.
- Raghavendra Chalapathy, Edward Toth, and Sanjay Chawla. 2018. Group anomaly detection using deep generative models. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 173--189.Google Scholar
- O.Y. Ercoskun. 2011. Green and Ecological Technologies for Urban Planning: Creating Smart Cities: Creating Smart Cities. Information Science Reference.Google Scholar
- Ralph Foorthuis. 2021. On the nature and types of anomalies: A review of deviations in data. International Journal of Data Science and Analytics 12, 4 (2021), 297--331.Google ScholarCross Ref
- Thomas Koch and Elenna R Dugundji. 2021. Taste variation in environmental features of bicycle routes. In Proceedings of the 14th ACM SIGSPATIAL International Workshop on Computational Transportation Science. 1--10.Google ScholarDigital Library
- Adittya Kuppa, Slawomir Grzonkowski, Muhammad Rizwan Asghar, and Nhien-An Le-Khac. 2019. Finding Rats in Cats: Detecting Stealthy Attacks using Group Anomaly Detection. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) (2019), 442--449.Google ScholarCross Ref
- Krikamol Muandet and Bernhard Schölkopf. 2013. One-class support measure machines for group anomaly detection. arXiv preprint arXiv:1303.0309 (2013).Google ScholarDigital Library
- Bernhard Schölkopf, John C. Platt, John Shawe-Taylor, Alex Smola, and Robert C. Williamson. 2001. Estimating the Support of a High-Dimensional Distribution. Neural Computation 13 (2001), 1443--1471.Google ScholarDigital Library
- Edward Toth and Sanjay Chawla. 2018. Group deviation detection methods: a survey. ACM Computing Surveys (CSUR) 51, 4 (2018), 1--38.Google ScholarDigital Library
- Weng-Keen Wong, Andrew Moore, Gregory Cooper, and Michael Wagner. 2003. WSARE: what's strange about recent events? Journal of Urban Health 80, 1 (2003), i66--i75.Google ScholarCross Ref
- Liang Xiong, Barnabás Póczos, and Jeff G. Schneider. 2011. Group Anomaly Detection using Flexible Genre Models. In NIPS.Google ScholarDigital Library
Index Terms
- Group anomaly detection for spatio-temporal collective behaviour scenarios in smart cities
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