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.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Hawkins, D.: Identification of Outliers. Chapman and Hall, London (1980)
Sharma, A.B., Golubchik, L., Govindan, R.: Sensor faults: detection methods and prevalence in real-world datasets. ACM Trans. Sens. Netw. 6(3), 23:1–23:39 (2010)
Aggarwal, C.C., Yu, P.S.: Outlier detection with uncertain data. In: SIAM ICDM, pp. 483–493 (2008)
Wang, B., Xiao, G., Yu, H., Yang, X.: Distance-based outlier detection on uncertain data. In: IEEE 9th ICCIT, pp. 293–298 (2009)
Shaikh, S.A., Kitagawa, H.: Efficient Distance-based Outlier Detection on Uncertain Datasets of Gaussian Distribution. In: World Wide Web, pp. 1–28 (2013)
Shaikh, S.A., Kitagawa, H.: Continuous Outlier Detection on Uncertain Data Streams. In: Proc. of IEEE 9th ISSNIP (2014)
Knorr, E.M., Ng, R.T., Tucakov, V.: Distance-Based Outliers: Algorithms and Applications. The VLDB Journal (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)