QS-STT: QuadSection clustering and spatial-temporal trajectory model for location prediction
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Location prediction is a crucial need for location-aware services and applications. Given an object’s recent movement and a future time, the goal of location prediction is to predict the location of the object at the future time specified. Different from traditional location prediction using motion function, some research works have elaborated on mining movement behavior from historical trajectories for location prediction. Without loss of generality, given a set of trajectories of an object, prior works on mining movement behaviors will first extract regions of popularity, in which the object frequently appears, and then discover the sequential relationships among regions. However, the quality of the frequent regions extracted affects the accuracy of the location prediction. Furthermore, trajectory data has both spatial and temporal information. To further enhance the accuracy of location prediction, one could utilize not only spatial information but also temporal information to predict the locations of objects. In this paper, we propose a framework QS-STT (standing for QuadSection clustering and Spatial-Temporal Trajectory model) to capture the movement behaviors of objects for location prediction. Specifically, we have developed QuadSection clustering to extract a reasonable and near-optimal set of frequent regions. Then, based on the set of frequent regions, we propose a spatial-temporal trajectory model to explore the object’s movement behavior as a probabilistic suffix tree with both spatial and temporal information of movements. Note that STT is not only able to discover sequential relationships among regions but also derives the corresponding probabilities of time, indicating when the object appears in each region. Based on STT, we further propose an algorithm to traverse STT for location prediction. By enhancing the quality of the frequent region extracted and exploring both the spatial and temporal information of STT, the accuracy of location prediction in QS-STT is improved. QS-STT is designed for individual location prediction. For verifying the effectiveness of QS-STT for location prediction under the different spatial density, we have conducted experiments on four types of real trajectory datasets with different speed. The experimental results show that our proposed QS-STT is able to capture both spatial and temporal patterns of movement behaviors and by exploring QS-STT, our proposed prediction algorithm outperforms existing works.
- Bejerano, G., Yona, G. (1999) Modeling protein families using probabilistic suffix trees. Proc. of RECOMB. pp. 15-24
- Hung, C.-W.C.C.-C., Peng, W.-C. (2009) Mining trajectory profiles for discovering user communities. Proc. of GIS-LBSN.
- Cao, X., Cong, G., Jensen, C.S. (2010) Mining significant semantic locations from gps data. PVLDB 3: pp. 1009-1020
- Giannotti, F., Nanni, M., Pedreschi, D. (2006) Efficient mining of temporally annotated sequences. Proc. of SDM.
- Giannotti, F., Nanni, M., Pinelli, F., Pedreschi, D. (2007) Trajectory pattern mining. Proc. of KDD.
- Gonzalez, M., Hidalgo, C., Barabási, A. (2008) Understanding individual human mobility patterns. Nature 453: pp. 779-782 CrossRef
- Guyet, T., Quiniou, R. (2008) Mining temporal patterns with quantitative intervals. Proc. of ICDM Workshops. pp. 218-227
- Hinneburg, A., Keim, D.A. (1998) An efficient approach to clustering in large multimedia databases with noise. Proc. of KDD. pp. 58-65
- Ishikawa, Y., Tsukamoto, Y., Kitagawa, H. (2004) Extracting mobility statistics from indexed spatio-temporal datasets. Proc. of STDBM. pp. 9-16
- Jeung, H., Liu, Q., Shen, H.T., Zhou, X. (2008) A hybrid prediction model for moving objects. Proc. of ICDE.
- Jeung, H., Shen, H.T., Zhou, X. (2007) Mining trajectory patterns using hidden Markov models. Proc. of DaWaK.
- Krumm, J., Horvitz, E. (2006) Predestination: inferring destinations from partial trajectories. Proc. of UbiComp.
- Lee, J.-G., Han, J., Li, X., Gonzalez, H. (2008) TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering. PVLDB 1: pp. 1081-1094
- Lei, P.-R., Shen, T.-J., Peng, W.-C., Su, I.-J. (2011) Exploring spatial-temporal trajectory model for location prediction. Proc. of MDM. pp. 58-67
- Lo, C.-H., Peng, W.-C., Chen, C.-W., Lin, T.-Y., Lin, C.-S. (2008) CarWeb: A traffic data collection platform. Proc. of MDM.
- Lu, C.-T., Lei, P.-R., Peng, W.-C., Su, I.-J. (2011) A framework of mining semantic regions from trajectories. Proc. of DASFAA. pp. 193-207
- Mamoulis, N., Cao, H., Kollios, G., Hadjieleftheriou, M., Tao, Y., Cheung, D.W. (2004) Mining, indexing, and querying historical spatiotemporal data. Proc. of KDD.
- Monreale, A., Pinelli, F., Trasarti, R., Giannotti, F. (2009) Wherenext: a location predictor on trajectory pattern mining. Proc. of KDD. pp. 637-646 CrossRef
- Montoliu, R., Gatica-Perez, D. (2010) Discovering human places of interest from multimodal mobile phone data. Proc. of MUM. pp. 12-21
- Morzy, M. (2007) Mining frequent trajectories of moving objects for location prediction. Proc. of MLDM. pp. 667-680
- Ostle, B., Malone, L. (1988) Statistics in Research: Basic Concepts and Techniques for Research Workers. Iowa State University Press, Ames
- Sun, S.C.P., Arunasalam, B. (2006) Mining for outliers in sequential databases. Proc. of SDM.
- Peng, W.-C., Ko, Y.-Z., Lee, W.-C. (2006) On mining moving patterns for object tracking sensor networks. Proc. of MDM.
- Ron, D., Singer, Y., Tishby, N. (1996) The power of amnesia: learning probabilistic automata with variable memory length. Mach. Learn. 25: pp. 117-149 CrossRef
- Tan, P.-N., Steinbach, M., Kumar, V. (2005) Introduction to Data Mining. Addison-Wesley, Boston
- Tsai, H.-P., Yang, D.-N., Peng, W.-C., Chen, M.-S. (2007) Exploring group moving pattern for an energy-constrained object tracking sensor network. Proc. of PAKDD.
- Ishikawa, Y.T.Y., Kitagawa, H. (2004) Extracting mobility statistics from indexed spatio-temporal datasets. Proc. of STDBM. pp. 9-16
- Yang, J., Wang, W. (2004) Agile: A general approach to detect transitions in evolving data streams. Proc. of ICDM.
- Ying, J.J.-C., Lu, E.H.-C., Lee, W.-C., Weng, T.-C., Tseng, V.S. (2010) Mining user similarity from semantic trajectories. Proc. of GIS-LBSN. pp. 19-26 CrossRef
- Yu, X., Pan, A., Tang, L.A., Li, Z., Han, J. (2011) Geo-friends recommendation in gps-based cyber-physical social network. Proc. of ASONAM. pp. 361-368
- Zheng, K., Trajcevski, G., Zhou, X., Scheuermann, P. (2011) Probabilistic range queries for uncertain trajectories on road networks. Proc. of EDBT. pp. 283-294
- QS-STT: QuadSection clustering and spatial-temporal trajectory model for location prediction
Distributed and Parallel Databases
Volume 31, Issue 2 , pp 231-258
- Cover Date
- Print ISSN
- Online ISSN
- Springer US
- Additional Links
- Trajectory pattern
- Movement behavior mining
- Location prediction
- Frequent region
- Spatial-temporal data
- Industry Sectors