ABSTRACT
Analyzing tracking data of various types of moving objects is an interesting research problem with numerous real-world applications. Several works have focused on continuously monitoring the nearest neighbors of a moving object, while others have proposed similarity measures for finding similar trajectories in databases containing historical tracking data. In this work, we introduce the problem of continuously monitoring nearest trajectories. In contrast to other similar approaches, we are interested in monitoring moving objects taking into account at each timestamp not only their current positions but their recent trajectory in a defined time window. We first describe a generic baseline algorithm for this problem, which applies for any aggregate function used to compute trajectory distances between objects, and without any restrictions on the movement of the objects. Using this as a framework, we continue to derive an optimized algorithm for the cases where the distance between two moving objects in a time window is determined by their maximum or minimum distance in all contained timestamps. Furthermore, we propose additional optimizations for the case that an upper bound on the velocities of the objects exists. Finally, we evaluate the efficiency of our proposed algorithms by conducting experiments on three real-world datasets.
- S. Babu and J. Widom. Continuous queries over data streams. SIGMOD Record, 30(3), 2001. Google ScholarDigital Library
- P. Bakalov and V. J. Tsotras. Continuous spatiotemporal trajectory joins. In GSN, 2006.Google Scholar
- R. Benetis, C. S. Jensen, G. Karciauskas, and S. Saltenis. Nearest and reverse nearest neighbor queries for moving objects. VLDB J., 15(3), 2006. Google ScholarDigital Library
- L. Chen, M. T. Özsu, and V. Oria. Robust and fast similarity search for moving object trajectories. In SIGMOD, 2005. Google ScholarDigital Library
- E. Frentzos, K. Gratsias, N. Pelekis, and Y. Theodoridis. Algorithms for nearest neighbor search on moving object trajectories. GeoInformatica, 11(2), 2007. Google ScholarDigital Library
- Y. Gao, C. Li, G. Chen, L. Chen, X. Jiang, and C. Chen. Efficient k-nearest-neighbor search algorithms for historical moving object trajectories. J. Comput. Sci. Technol., 22(2), 2007. Google ScholarDigital Library
- J. Gudmundsson and M. J. van Kreveld. Computing longest duration flocks in trajectory data. In GIS, 2006. Google ScholarDigital Library
- R. H. Güting, T. Behr, and J. Xu. Efficient k-nearest neighbor search on moving object trajectories. VLDB J., 19(5), 2010. Google ScholarDigital Library
- Y.-K. Huang, S.-J. Liao, and C. Lee. Efficient continuous k-nearest neighbor query processing over moving objects with uncertain speed and direction. In SSDBM, 2008. Google ScholarDigital Library
- G. S. Iwerks, H. Samet, and K. P. Smith. Continuous k-nearest neighbor queries for continuously moving points with updates. In VLDB, 2003. Google ScholarDigital Library
- H. Jeung, M. L. Yiu, X. Zhou, C. S. Jensen, and H. T. Shen. Discovery of convoys in trajectory databases. PVLDB, 1(1), 2008. Google ScholarDigital Library
- P. Kalnis, N. Mamoulis, and S. Bakiras. On discovering moving clusters in spatio-temporal data. In SSTD, 2005. Google ScholarDigital Library
- G. Kollios, D. Gunopulos, and V. J. Tsotras. Nearest neighbor queries in a mobile environment. In STDBM, 1999. Google ScholarDigital Library
- J.-G. Lee, J. Han, and K.-Y. Whang. Trajectory clustering: a partition-and-group framework. In SIGMOD, 2007. Google ScholarDigital Library
- Z. Li, B. Ding, J. Han, and R. Kays. Swarm: Mining relaxed temporal moving object clusters. PVLDB, 3(1), 2010. Google ScholarDigital Library
- B. Lin and J. Su. Shapes based trajectory queries for moving objects. In GIS, 2005. Google ScholarDigital Library
- K. Mouratidis, M. Hadjieleftheriou, and D. Papadias. Conceptual partitioning: An efficient method for continuous nearest neighbor monitoring. In SIGMOD, 2005. Google ScholarDigital Library
- J. Niedermayer, A. Züfle, T. Emrich, M. Renz, N. Mamoulis, L. Chen, and H.-P Kriegel. Probabilistic nearest neighbor queries on uncertain moving object trajectories. PVLDB, 7(3), 2013. Google ScholarDigital Library
- N. Pelekis, I. Kopanakis, G. Marketos, I. Ntoutsi, G. L. Andrienko, and Y. Theodoridis. Similarity search in trajectory databases. In TIME, 2007. Google ScholarDigital Library
- D. Pfoser, C. S. Jensen, and Y. Theodoridis. Novel approaches in query processing for moving object trajectories. In VLDB, 2000. Google ScholarDigital Library
- G. Skoumas, D. Skoutas, and A. Vlachaki. Efficient identification and approximation of k-nearest moving neighbors. In SIGSPATIAL/GIS, 2013. Google ScholarDigital Library
- Z. Song and N. Roussopoulos. K-nearest neighbor search for moving query point. In SSTD, 2001. Google ScholarDigital Library
- Y. Tao and D. Papadias. Time-parameterized queries in spatio-temporal databases. In SIGMOD, 2002. Google ScholarDigital Library
- Y. Tao, D. Papadias, and Q. Shen. Continuous nearest neighbor search. In VLDB, 2002. Google ScholarDigital Library
- G. Trajcevski, R. Tamassia, H. Ding, P. Scheuermann, and I. F. Cruz. Continuous probabilistic nearest-neighbor queries for uncertain trajectories. In EDBT, 2009. Google ScholarDigital Library
- M. Vazirgiannis, Y. Theodoridis, and T. K. Sellis. Spatio-temporal composition and indexing for large multimedia applications. Multimedia Syst., 6(4), 1998. Google ScholarDigital Library
- M. R. Vieira, P. Bakalov, and V. J. Tsotras. On-line discovery of flock patterns in spatio-temporal data. In GIS, 2009. Google ScholarDigital Library
- M. Vlachos, D. Gunopulos, and G. Kollios. Discovering similar multidimensional trajectories. In ICDE, 2002. Google ScholarDigital Library
- X. Xiong, M. F. Mokbel, and W. G. Aref. Sea-cnn: Scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases. In ICDE, 2005. Google ScholarDigital Library
- Y Yanagisawa, J. Akahani, and T. Satoh. Shape-based similarity query for trajectory of mobile objects. In MDM, 2003. Google ScholarDigital Library
- X. Yu, K. Q. Pu, and N. Koudas. Monitoring k-nearest neighbor queries over moving objects. In ICDE, 2005. Google ScholarDigital Library
- J. Yuan, Y. Zheng, X. Xie, and G. Sun. Driving with knowledge from the physical world. In KDD, 2011. Google ScholarDigital Library
- J. Yuan, Y. Zheng, C. Zhang, W. Xie, X. Xie, G. Sun, and Y. Huang. T-drive: driving directions based on taxi trajectories. In GIS, 2010. Google ScholarDigital Library
- J. Zhang, M. Zhu, D. Papadias, Y. Tao, and D. L. Lee. Location-based spatial queries. In SIGMOD, 2003. Google ScholarDigital Library
Index Terms
- Continuous monitoring of nearest trajectories
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