Abstract
With recent advances in sensory and mobile computing technology, many interesting applications involving moving objects have emerged. One of them is identification of suspicious movements: an important problem in homeland security. The objects in question can be vehicles, airplanes, or ships; however, due to the sheer volume of data and the complexities within, manual inspection of the moving objects would require too much manpower. Thus, an automated or semi-automated solution to this problem would be very helpful. That said, it is challenging to develop a method that can efficiently and effectively detect anomalies. The problem is exacerbated by the fact that anomalies may occur at arbitrary levels of abstraction and be associated with multiple granularity of spatiotemporal features.
In this study, we propose a new framework named ROAM ( Rule- and Motif-based Anomaly Detection in Moving Objects). In ROAM, object trajectories are expressed using discrete pattern fragments called motifs. Associated features are extracted to form a hierarchical feature space, which facilitates a multi-resolution view of the data. We also develop a general-purpose, rule-based classifier which explores the structured feature space and learns effective rules at multiple levels of granularity. Such rules are easily readable and can be easily provided to humans to aid better inspection of moving objects.
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References
Agrawal, R., Psaila, G., Wimmers, E.L., Zait, M.: Querying shapes of histories. In: Proceedings of 1995 Int. Conf. Very Large Data Bases (VLDB 1995), Zurich, Switzerland, September 1995, pp. 502–514 (1995)
Barnett, V., Lewis, T.: Outliers in Statistical Data. John Wiley & Sons, Chichester (1994)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Cao, H., Wolfson, O.: Nonmaterialized motion information in transport networks. In: Eiter, T., Libkin, L. (eds.) ICDT 2005. LNCS, vol. 3363, pp. 173–188. Springer, Heidelberg (2005)
Chiu, B., Keogh, E., Lonardi, S.: Probabilistic discovery of time series motifs. In: Proceedings of 2003 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 2003), Washington, DC (August 2003)
Denis, F.: Pac learning from positive statistical queries. In: Richter, M.M., Smith, C.H., Wiehagen, R., Zeugmann, T. (eds.) ALT 1998. LNCS (LNAI), vol. 1501. Springer, Heidelberg (1998)
Forlizzi, L., Güting, R.H., Nardelli, E., Schneider, M.: A data model and data structures for moving objects databases. In: Proceedings of 2000 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD 2000), Dallas, TX, May 2000, pp. 319–330 (2000)
Gaffney, S., Smyth, P.: Trajectory clustering with mixtures of regression models. In: Proceedings of 1999 Int. Conf. Knowledge Discovery and Data Mining (KDD 1999), pp. 63–72 (1999)
Güting, R.H., Schneider, M.: Moving Objects Databases. Morgan Kaufmann, San Francisco (2005)
Güting, R.H., Bohlen, M.H., Erwig, M., Jensen, C.S., Lorentzos, N.A., Schneider, M., Vazirgiannis, M.: A foundation for representing and querying moving objects. ACM Trans. Database Systems (TODS) (March 2000)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Kalnis, P., Mamoulis, N., Bakiras, S.: On discovering moving clusters in spatio-temporal data. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 364–381. Springer, Heidelberg (2005)
Keogh, E., Pazzani, M.: An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. In: Proceedings of 1998 Int. Conf. Knowledge Discovery and Data Mining (KDD 1998), New York, NY, August 1998, pp. 239–243 (1998)
Knorr, E., Ng, R.: Algorithms for mining distance-based outliers in large datasets. In: Proceedings of 1998 Int. Conf. Very Large Data Bases (VLDB 1998), New York, NY, August 1998, pp. 392–403 (1998)
Koperski, K., Han, J.: Discovery of spatial association rules in geographic information databases. In: Egenhofer, M.J., Herring, J.R. (eds.) SSD 1995. LNCS, vol. 951, pp. 47–66. Springer, Heidelberg (1995)
Kostov, V., Ozawa, J., Yoshioka, M., Kudoh, T.: Travel destination prediction using frequent crossing pattern from driving history. In: Proceedings of 8th Int. IEEE Conf. Intelligent Transportation Systems, Vienna, Austria, September 2005, pp. 970–977 (2005)
Liao, L., Fox, D., Kautz, H.: Learning and inferring transportation routines. In: Proceedings of 2004 Nat. Conf. Artificial Intelligence (AAAI 2004) (2004)
Liu, H., Hussain, F., Tan, C.L., Dash, M.: Discretization: An enabling technique. Data Mining and Knowledge Discover 6, 393–423 (2002)
Pfoser, D., Jensen, C.S., Theodoridis, Y.: Novel approaches to the indexing of moving object trajectories. In: Proceedings of 2000 Int. Conf. Very Large Data Bases (VLDB 2000), Cairo, Egypt, September 2000, pp. 395–406 (2000)
Quinlan, J.R., Cameron-Jones, R.M.: FOIL: A midterm report. In: Proceedings of 1993 European Conf. Machine Learning, Vienna, Austria, pp. 3–20 (1993)
Saltenis, S., Jensen, C.S., Leutenegger, S.T., Lopez, M.A.: Indexing the positions of continuously moving objects. In: Proceedings of 2000 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD 2000), Dallas, TX, May 2000, pp. 331–342 (2000)
Theodoridis, Y., Silva, J.R.O., Nascimento, M.A.: On the generation of spatiotemporal datasets. In: Güting, R.H., Papadias, D., Lochovsky, F.H. (eds.) SSD 1999. LNCS, vol. 1651. Springer, Heidelberg (1999)
Tsoukatos, I., Gunopulos, D.: Efficient mining of spatiotemporal patterns. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds.) SSTD 2001. LNCS, vol. 2121, pp. 425–442. Springer, Heidelberg (2001)
Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)
Wei, L., Keogh, E.: Semi-supervised time series classification. In: Proceedings of 2006 Int. Conf. Knowledge Discovery and Data Mining (KDD 2006), Philadelphia, PA (August 2006)
Xi, X., Keogh, E., Shelton, C., Wei, L.: Fast time series classification using numerosity reduction. In: Proceedings of 2006 Int. Conf. Machine Learning (ICML 2006) (2006)
Yin, X., Han, J.: CPAR: Classification based on predictive association rules. In: Proceedings of 2003 SIAM Int. Conf. Data Mining (SDM 2003), San Fransisco, CA, May 2003, pp. 331–335 (2003)
Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: an efficient data clustering method for very large databases. In: Proceedings of 1996 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD 1996), Montreal, Canada, June 1996, pp. 103–114 (1996)
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Li, X., Han, J., Kim, S., Gonzalez, H. (2008). Anomaly Detection in Moving Object. In: Chen, H., Yang, C.C. (eds) Intelligence and Security Informatics. Studies in Computational Intelligence, vol 135. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69209-6_19
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DOI: https://doi.org/10.1007/978-3-540-69209-6_19
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