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Segmentation and Sampling of Moving Object Trajectories Based on Representativeness
July 2012 (vol. 24 no. 7)
pp. 1328-1343
Costas Panagiotakis, Tech. Educational Inst. of Crete, Ierapetra
Nikos Pelekis, University of Piraeus, Piraeus
Ioannis Kopanakis, Tech. Educational Inst. of Crete, Ierapetra
Emmanuel Ramasso, FEMTO-ST Research Inst., Besançon
Yannis Theodoridis, University of Piraeus, Piraeus
Moving Object Databases (MOD), although ubiquitous, still call for methods that will be able to understand, search, analyze, and browse their spatiotemporal content. In this paper, we propose a method for trajectory segmentation and sampling based on the representativeness of the (sub)trajectories in the MOD. In order to find the most representative subtrajectories, the following methodology is proposed. First, a novel global voting algorithm is performed, based on local density and trajectory similarity information. This method is applied for each segment of the trajectory, forming a local trajectory descriptor that represents line segment representativeness. The sequence of this descriptor over a trajectory gives the voting signal of the trajectory, where high values correspond to the most representative parts. Then, a novel segmentation algorithm is applied on this signal that automatically estimates the number of partitions and the partition borders, identifying homogenous partitions concerning their representativeness. Finally, a sampling method over the resulting segments yields the most representative subtrajectories in the MOD. Our experimental results in synthetic and real MOD verify the effectiveness of the proposed scheme, also in comparison with other sampling techniques.

[1] R.H. Guting and M. Schneider, Moving Object Databases. Morgan Kaufmann Publishers, 2005.
[2] F. Giannotti and D. Pedreschi, Mobility, Data Mining and Privacy, Geographic Knowledge Discovery. Springer-Verlag, 2008.
[3] M. Hadjieleftheriou, G. Kollios, V. Tsotras, and D. Gunopulos, "Efficient Indexing of Spatiotemporal Objects," Proc. Int'l Conf. Extending Database Technology (EDBT), 2002.
[4] J. Han, J.-G. Lee, and K.-Y. Whang, "Trajectory Clustering: A Partition-and-Group Framework," Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD), pp. 593-604, 2007.
[5] J.-G. Lee, J. Han, X. Li, and H. Gonzalez, "Traclass: Trajectory Classification Using Hierarchical Region-Based and Trajectory-Based Clustering," Proc. VLDB Endowment, vol. 1, pp. 1081-1094, 2008.
[6] A. Anagnostopoulos, M. Vlachos, M. Hadjieleftheriou, E. Keogh, and P.S. Yu, "Global Distance-Based Segmentation of Trajectories," Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD), pp. 34-43, 2006.
[7] L. Chen, M.T. Özsu, and V. Oria, "Robust and Fast Similarity Search for Moving Object Trajectories," Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD), pp. 491-502, 2005.
[8] M. Nanni and D. Pedreschi, "Time-Focused Clustering of Trajectories of Moving Objects," J. Intelligent Information Systems, vol. 27, no. 3, pp. 267-289, 2006.
[9] N. Pelekis, I. Kopanakis, G. Marketos, I. Ntoutsi, G. Andrienko, and Y. Theodoridis, "Similarity Search in Trajectory Databases," Proc. Int'l Symp. Temporal Representation and Reasoning (TIME), pp. 129-140, 2007.
[10] M. Benkert, J. Gudmundsson, F. Hubner, and T. Wolle, "Reporting Flock Patterns," Proc. Conf. Ann. European Symp. (ESA), pp. 660-671, 2006.
[11] Y. Li, J. Han, and J. Yang, "Clustering Moving Objects," Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD), pp. 617-622, 2004.
[12] N. Pelekis, I. Kopanakis, E.E. Kotsifakos, E. Frentzos, and Y. Theodoridis, "Clustering Trajectories of Moving Objects in an Uncertain World," Proc. Int'l Conf. Data Mining (ICDM), 2009.
[13] G. Kollios, D. Gunopulos, N. Koudas, and S. Berchtold, "Efficient Biased Sampling for Approximate Clustering and Outlier Detection in Large Data Sets," IEEE Trans. Knowledge and Data Eng., vol. 15, no. 5, pp. 1170-1187, Sept./Oct. 2003.
[14] A. Nanopoulos, Y. Theodoridis, and Y. Manolopoulos, "Indexed-Based Density Biased Sampling for Clustering Applications," Data and Knowledge Eng., vol. 57, no. 1, pp. 37-63, 2006.
[15] G. Andrienko, N. Andrienko, S. Rinzivillo, M. Nanni, and D. Pedreschi, "A Visual Analytics Toolkit for Cluster-Based Classification of Mobility Data," Proc. Int'l Symp. Advances in Spatial and Temporal Databases (SSTD), pp. 432-435, 2009.
[16] G. Andrienko, N. Andrienko, S. Rinzivillo, M. Nanni, D. Pedreschi, and F. Giannotti, "Interactive Visual Clustering of Large Collections of Trajectories," Proc. IEEE Symp. Visual Analytics Science and Technology (VAST), pp. 3-10, 2009.
[17] N. Pelekis, C. Panagiotakis, I. Kopanakis, and Y. Theodoridis, "Unsupervised Trajectory Sampling," Proc. European Conf. Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), 2010.
[18] H. Jeung, M.L. Yiu, X. Zhou, C. Jensen, and H.T. Shen, "Discovery of Convoys in Trajectory Databases," Proc. VLDB Endowment, vol. 1, pp. 1068-1080, 2008.
[19] G. Andrienko, N. Andrienko, and S. Wrobel, "Visual Analytics Tools for Analysis of Movement Data," SIGKDD Explorations Newsletter, vol. 9, no. 2, pp. 38-46, 2007.
[20] M. Vlachos, D. Gunopulos, and G. Das, "Rotation Invariant Distance Measures for Trajectories," Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD), pp. 707-712, 2004.
[21] E. Frentzos, K. Gratsias, and Y. Theodoridis, "Index-Based Most Similar Trajectory Search," Proc. Int'l Conf. Data Eng. (ICDE), 2007.
[22] M. Ankerst, M.M. Breunig, H.-P. Kriegel, and J. Sander, "Optics: Ordering Points to Identify the Clustering Structure," Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD), 1999.
[23] D. Sacharidis, K. Patroumpas, M. Terrovitis, V. Kantere, M. Potamias, K. Mouratidis, and T. Sellis, "On-line Discovery of Hot Motion Paths," Proc. Int'l Conf. Extending Database Technology (EDBT), pp. 392-403, 2008.
[24] C. Panagiotakis, N. Pelekis, and I. Kopanakis, "Trajectory Voting and Classification Based on Spatiotemporal Similarity in Moving Object Databases," Proc. Int'l Symp. Intelligent Data Analysis (IDA), pp. 131-142, 2009.
[25] D. Pfoser, C.S. Jensen, and Y. Theodoridis, "Novel Approaches to the Indexing of Moving Object Trajectories," Proc. VLDB Conf., 2000.
[26] E. Frentzos, K. Gratsias, N. Pelekis, and Y. Theodoridis, "Algorithms for Nearest Neighbor Search on Moving Object Trajectories," GeoInformatica, vol. 11, pp. 159-193, 2007.
[27] Y. Theodoridis, M. Vazirgiannis, and T. Sellis, "Spatio-temporal Indexing for Large Multimedia Applications," Proc. Int'l Conf. Multimedia Computing and Systems (ICMCS), 1996.
[28] D. Patterson, Artificial Neural Networks. Prentice Hall, 1996.
[29] K. Wang, J. Yuan, L. Bo, and T. Yu, "Adaptive Spherical Gaussian Kernel in Sparse Bayesian Learning Framework for Nonlinear Regression," Expert Systems with Applications, vol. 36, no. 2, pp. 3982-3989, 2009.
[30] N. Meratnia and R. By, "Spatiotemporal Compression Techniques for Moving Point Objects," Proc. Int'l Conf. Extending Database Technology (EDBT), 2004.
[31] C. Panagiotakis and G. Tziritas, "A Speech/Music Discriminator Based on RMS and Zero-Crossings," IEEE Trans. Multimedia, vol. 7, no. 1, pp. 155-166, Feb. 2005.
[32] C. Panagiotakis, E. Kokinou, and F. Vallianatos, "Automatic P-Phase Picking Based on Local-Maxima Distribution," IEEE Trans. Geoscience and Remote Sensing, vol. 46, no. 8, pp. 2280-2287, Aug. 2008.
[33] P.C. Mahalanobis, "On the Generalized Distance in Statistics," Proc. Nat'l Inst. of Science India, vol. 2, pp. 49-55, 1936.
[34] S.K. Zhou and R. Chellappa, "From Sample Similarity to Ensemble Similarity: Probabilistic Distance Measures in Reproducing Kernel Hilbert Space," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 6, pp. 917-929, June 2006.
[35] T. Brinkhoff, "A Framework for Generating Network-Based Moving Objects," GeoInformatica, vol. 6, no. 2, pp. 153-180, 2002.
[36] http://www.rtreeportal.orgindex.php?option=com_content= task=view=id=30=Itemid=43 , 2012.
[37], 2012.
[38] S.K. Thompson, Sampling. Wiley-Interscience, 2002.
[39] J.-G. Lee, J. Han, and X. Li, "Trajectory Outlier Detection: A Partition-and-Detect Framework," Proc. IEEE 24th Int'l Conf. Data Eng. (ICDE), pp. 140-149, 2008.

Index Terms:
Trajectory segmentation, subtrajectory sampling, data mining, moving object databases.
Costas Panagiotakis, Nikos Pelekis, Ioannis Kopanakis, Emmanuel Ramasso, Yannis Theodoridis, "Segmentation and Sampling of Moving Object Trajectories Based on Representativeness," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 7, pp. 1328-1343, July 2012, doi:10.1109/TKDE.2011.39
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