Abstract
Searching similar trajectories in real time has been a challenging task in a large variety of location-aware applications. This paper addresses its two key issues, i.e. evaluating the similarity between two trajectories reasonably and effectively, and providing efficient algorithms to support queries in real time. Firstly, a novel similarity measurement, called Global Temporal Similarity (GTS), is suggested, which is perturbation-free and effective since it takes into account both the evolution of the similarity over time and the spatial movements. Secondly, a new index structure with linear updated time, called Real Time Similar Trajectory Searching-tree (RTSTS-tree), is proposed to support the search of similar trajectories. Besides, to support k Nearest Neighbor (kNN) query of trajectories and Top k Similar Pairs query, two algorithms are proposed based on GTS and RTSTS-tree and are capable of searching similar trajectories by object and by location with the time complexity of O(n) and O(n 2) respectively. Finally, the results of the extensive experiments conducted on real and synthetic data set validate the effectiveness and the efficiency of the proposed similarity measurement, index structure and query algorithms.
Supported by the Central University Fundamental Science Research Foundation of China under Grant No. 2010SCU11053.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Frentzos, E., Gratsias, K., Theodoridis, Y.: Index-based Most Similar Trajectory Search. In: 23th IEEE International Conference on Data Engineering, ICDE (2007)
Agrawal, R., Faloutsos, C., Swami, A.: Efficient Similarity Search in Sequence Databases. In: Lomet, D.B. (ed.) FODO 1993. LNCS, vol. 730, pp. 69–84. Springer, Heidelberg (1993)
Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. In: Proceedings of SIGMOD 1994, pp. 419–429 (1994)
Cai, Y., Ng, R.: Indexing spatio-temporaltrajectories with chebyshev polynomials. In: Proceedings of SIGMOD 2005, pp. 599–610 (2004)
Berndt, J., Clifford, J.: Finding patterns in time series: Adynamic programming approach. In: Advances in Knowledge Discovery and Data Mining, pp. 229–248. AAAI/MIT Press, Menlo Park, CA (1996)
Bollobas, B., Das, G., Gunopulos, D., Mannila, H.: Time-Series Similarity Problems and Well-Separated Geometric Sets. Nordic Journal of Computing (2001)
Chen, L., Özsu Tamer, M., Oria, V.: Robust and Fast Similarity Search for Moving Object Trajectories. In: Proceedings of SIGMOD 2005 (2005)
Chang, J., Song, M., Um, J.: TMN-tree - New Trajectory Index Structure for Moving Objects in Spatial Networks. In: 10th IEEE International Conference on Computer and Information Technology, CIT 2010 (2010)
Almeida, V., Güting, R.: Indexing the Trajectories of Moving Objects in Networks. Proceedings of GeoInformatica 9(1), 33–60 (2005)
Yu, C., Ooi, B., Tan, K., Jagadish, H.: Indexing the Distance: AnEfficient Method to KNN Processing. In: Proceedings of VLDB 2001, pp. 421–430 (September 2001)
Frentzos, E.: Indexing Objects Moving on Fixed Networks. In: Hadzilacos, T., Manolopoulos, Y., Roddick, J., Theodoridis, Y. (eds.) SSTD 2003. LNCS, vol. 2750, pp. 289–305. Springer, Heidelberg (2003)
Guttman, A.: R-Trees: A Dynamic Index Structure for Spatial Searching. In: Proceedings of ACM SIGMOD, pp. 47–57 (1984)
Sellis, T., Roussopoulos, N., Faloutsos, C.: The R + -Tree: A Dynamic Index for Multi-Dimensional Objects. In: Proceedings of VLDB 1987, pp. 507–518 (1987)
Beckmann, N., Kriegel, H., Schneider, R.: The R*-tree: an efficient and robust access method for points and rectangles. In: SIGMOD 1999, pp. 322–331 (1999)
Pfoser, D., Jensen, C., Theodoridis, Y.: Novel Approach to theIndexing of Moving Object Trajectories. In: Proceedings of VLDB 2000, pp. 395–406 (2000)
Vazirgiannis, M., Theodoridis, Y., Sellis, T.: Spatio-temporal Indexing for Large Multimedia Applications. In: Proceedings of the IEEE Conference on Multimedia Computing and Systems, vol. 6(4), pp. 284–298 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ma, Y., Qu, C., Liu, T., Yang, N., Tang, C. (2012). Searching Similar Trajectories in Real Time: An Effectiveness and Efficiency Study. In: Wang, L., Jiang, J., Lu, J., Hong, L., Liu, B. (eds) Web-Age Information Management. WAIM 2011. Lecture Notes in Computer Science, vol 7142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28635-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-28635-3_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28634-6
Online ISBN: 978-3-642-28635-3
eBook Packages: Computer ScienceComputer Science (R0)