Abstract
The advance of positioning technology enables us to online collect moving object data streams for many applications. One of the most significant applications is to detect emergency event through observed abnormal behavior of objects for disaster prediction. However, the continuously generated moving object data streams are often accumulated to a massive dataset in a few seconds and thus challenge existing data analysis techniques. In this paper, we model a process of emergency event forming as a process of rolling a snowball, that is, we compare a size-rapidly-changed (e.g., increased or decreased) group of moving objects to a snowball. Thus, the problem of emergency event detection can be resolved by snowball discovery. Then, we provide two algorithms to find snowballs: a clustering-and-scanning algorithm with the time complexity of O(n 2) and an efficient adjacency-list-based algorithm with the time complexity of O(nlogn). The second method adopts adjacency lists to optimize efficiency. Experiments on both real-world dataset and large synthetic datasets demonstrate the effectiveness, precision and efficiency of our algorithms
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References
Kalnis, P., Mamoulis, N., Bakiras, S.: On discovering moving clustering moving clusters in spatio-temporal data. In: Medeiros, C.B., Egenhofer, M., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 364–381. Springer, Heidelberg (2005)
Al-Naymat, G., Chawla, S., Gudmundsson, J.: Dimensionality reduction for long duration and complex spatio-temporal queries. In: SAC (2007)
Jeung, H., Shen, H.T., Zhou, X.: Convoy queries in spatio-temporal databases. In: ICDE (2008)
Tang, L.A., Zheng, Y.: On discovery of traveling companions from streaming trajectories. In: ICDE (2012)
Li, Z., Ding, B., Han, J., et al.: Swarm: Mining relaxed temporal moving object clusters. In: VLDB (2010)
Zheng, K., Zheng, Y., Yuan, N.J.: On Discovery of Gathering Patterns from Trajectories. In: ICDE (2013)
Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: An efficient data clustering method for very large databases. In: SIGMOD (1996)
Andersson, M., Gudmundsson, J., et al.: Reporting leadership patterns among trajectories. In: SAC (2007)
Laube, P., van Kreveld, M., Imfeld, S.: Finding REMO-detecting relative motion patterns in geospatial lifelines. In: SDH 2005, pp. 201–215. Springer, Heidelberg (2005)
Ni, J., Ravishankar, C.V.: Indexing spatio-temporal trajectories with efficient polynomial approximations. In: TKDE, vol. 19, pp. 663–678 (2007)
Anagnostopoulos, A., Vlachos, M., Hadjieleftheriou, M., et al.: Global distance-based segmentation of trajectories. In: SIGKDD, pp. 34–43 (2006)
Lee, J.-G., Han, J.: Trajectory clustering: A partition-and-group framework. In: SIGMOD (2007)
Wu, H.-R., Yeh, M.-Y., Chen, M.-S.: Profiling Moving Objects by Dividing and Clustering Trajectories Spatiotemporally. In: TKDE (2012)
Palma, A.T., Bogorny, V., Kuijpers, B., et al.: A clustering-based approach for discovering interesting places in trajectories. In: SAC, pp. 863–868 (2008)
Giannotti, F., Nanni, M., Pinelli, F., et al.: Trajectory pattern mining. In: SIGKDD, pp. 330–339 (2007)
Zheng, Y., Zhang, L., Xie, X., et al.: Mining interesting locations and travel sequences from gps trajectories. In: WWW, pp. 791–800 (2009)
Ester, M., Kriegel, H.-P., Sander, J., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: SIGKDD (1996)
Lelis, L., Sander, J.: Semi-supervised density-based clustering. In: ICDM, pp. 842–847 (2009)
Yang, D., Rundensteiner, E.A., Ward, M.O.: Summarization and Matching of Density-Based Clusters in Streaming Environments. In: VLDB, pp. 121–132 (2012)
Brinkhoff, T.: A framework for generating network-based moving objects. GeoInformatica 6(2) (2002)
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Guo, L., Huang, G., Ding, Z. (2014). Efficient Detection of Emergency Event from Moving Object Data Streams. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds) Database Systems for Advanced Applications. DASFAA 2014. Lecture Notes in Computer Science, vol 8422. Springer, Cham. https://doi.org/10.1007/978-3-319-05813-9_28
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DOI: https://doi.org/10.1007/978-3-319-05813-9_28
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05812-2
Online ISBN: 978-3-319-05813-9
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