Abstract
Trajectory data capture the traveling history of moving objects such as people or vehicles. With the proliferation of GPS and tracking technology, huge volumes of trajectories are rapidly generated and collected. Under this, applications such as route recommendation and traveling behavior mining call for efficient trajectory retrieval. In this paper, we first focus on distance-based trajectory search; given a collection of trajectories and a set query points, the goal is to retrieve the top-k trajectories that pass as close as possible to all query points. We advance the state-of-the-art by combining existing approaches to a hybrid method and also proposing an alternative, more efficient range-based approach. Second, we propose and study the practical variant of bounded distance-based search, which takes into account the temporal characteristics of the searched trajectories. Through an extensive experimental analysis with real trajectory data, we show that our range-based approach outperforms previous methods by at least one order of magnitude.
Work supported by grant HKU 715413E from Hong Kong RGC, and by the European Social Fund and Greek National Funds through the NSRF Research Program Thales.
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Zheng, Y.: Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. 6, 29:1–29:41 (2015)
Zheng, Y., Zhou, X. (eds.): Computing with Spatial Trajectories. Springer, New York (2011)
Chen, Z., Shen, H.T., Zhou, X., Zheng, Y., Xie, X.: Searching trajectories by locations: an efficiency study. In: SIGMOD, pp. 255–266 (2010)
Tang, L.-A., Zheng, Y., Xie, X., Yuan, J., Yu, X., Han, J.: Retrieving k-nearest neighboring trajectories by a set of point locations. In: Pfoser, D., Tao, Y., Mouratidis, K., Nascimento, M.A., Mokbel, M., Shekhar, S., Huang, Y. (eds.) SSTD 2011. LNCS, vol. 6849, pp. 223–241. Springer, Heidelberg (2011)
Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. In: PODS, pp. 102–113 (2001)
Ilyas, I.F., Beskales, G., Soliman, M.A.: A survey of top-k query processing techniques in relational database systems. ACM Comput. Surv. 40(4), 11:1–11:58 (2008)
Güntzer, U., Balke, W., Kießling, W.: Towards efficient multi-feature queries in heterogeneous environments. In: ITCC, pp. 622–628 (2001)
Güntzer, U., Balke, W.T., Kießling, W.: Optimizing multi-feature queries for image databases. In: VLDB, pp. 419–428 (2000)
Papadias, D., Tao, Y., Mouratidis, K., Hui, C.K.: Aggregate nearest neighbor queries in spatial databases. ACM Trans. Database Syst. 30(2), 529–576 (2005)
Zheng, Y., Zhang, L., Xie, X., Ma, W.Y.: Mining interesting locations and travel sequences from GPS trajectories. In: WWW 2009, pp. 791–800 (2009)
Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.: Understanding mobility based on GPS data. In: UbiComp 2008: Proceedings of the 10th International Conference on Ubiquitous Computing, UbiComp 2008, Seoul, Korea, 21–24 September, pp. 312–321 (2008)
Zheng, Y., Xie, X., Ma, W.: Geolife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 33(2), 32–39 (2010)
Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, San Jose, California, 22–25 May, pp. 71–79 (1995)
Hjaltason, G.R., Samet, H.: Distance browsing in spatial databases. ACM Trans. Database Syst. 24(2), 265–318 (1999)
Böhm, C., Berchtold, S., Keim, D.A.: Searching in high-dimensional spaces: index structures for improving the performance of multimedia databases. ACM Comput. Surv. 33(3), 322–373 (2001)
Jagadish, H.V., Ooi, B.C., Tan, K., Yu, C., Zhang, R.: iDistance: an adaptive b\({}^{\text{+ }}\)-tree based indexing method for nearest neighbor search. ACM Trans. Database Syst. 30(2), 364–397 (2005)
Tao, Y., Yi, K., Sheng, C., Kalnis, P.: Quality and efficiency in high dimensional nearest neighbor search. In: SIGMOD, pp. 563–576 (2009)
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Qi, S., Bouros, P., Sacharidis, D., Mamoulis, N. (2015). Efficient Point-Based Trajectory Search. In: Claramunt, C., et al. Advances in Spatial and Temporal Databases. SSTD 2015. Lecture Notes in Computer Science(), vol 9239. Springer, Cham. https://doi.org/10.1007/978-3-319-22363-6_10
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DOI: https://doi.org/10.1007/978-3-319-22363-6_10
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