Abstract
This paper describes and demonstrates MOOD, a system for detecting outliers from moving objects data. In particular, we demonstrate a continuous distance-based outlier detection approach for moving objects’ data streams. We assume that the moving objects are uncertain, as the state of a moving object can not be known precisely, and this uncertainty is given by the Gaussian distribution. The MOOD system provides an interface which takes moving objects’ states streams and some parameters as input and continuously produces the distance-based outliers along with some graphs comparing the efficiency and accuracy of the underlying algorithms.
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Shaikh, S.A., Kitagawa, H. (2014). MOOD: Moving Objects Outlier Detection. In: Chen, L., Jia, Y., Sellis, T., Liu, G. (eds) Web Technologies and Applications. APWeb 2014. Lecture Notes in Computer Science, vol 8709. Springer, Cham. https://doi.org/10.1007/978-3-319-11116-2_66
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DOI: https://doi.org/10.1007/978-3-319-11116-2_66
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11115-5
Online ISBN: 978-3-319-11116-2
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