Definition
The extraction of implicit, nontrivial, and potentially useful abstract information from large collections of spatio-temporal data are referred to as spatio-temporal data mining. There are two classes of spatio-temporal databases. The first category includes timestamped sequences of measurements generated by sensors distributed in a map and temporal evolutions of thematic maps (e.g., weather maps). The second class is moving object databases that consist of object trajectories (e.g., movements of cars in a city). A trajectory can be modeled as a sequence of (p i , t i ) pairs, where p i corresponds to a spatial location and t i is a timestamp. The management and analysis of spatio-temporal data has gained interest recently, mainly due to the rapid advancements in telecommunications (e.g., GPS, cellular networks, etc.), which facilitate the collection of large datasets of object locations (e.g., cars, mobile phone users) and...
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Recommended Reading
Berndt D, Clifford J. Using dynamic time warping to find patterns in time series. Proc. KDD Workshop; 1994.
Cao H, Mamoulis N, Cheung DW. Mining frequent spatio-temporal sequential patterns. Proc. 2005 IEEE Int. Conf. on Data Mining; 2005. p. 82–89.
Das G, Gunopulos D, Mannila H. Finding similar time series. Advances in knowledge discovery and data mining, 1st Pacific-Asia Conf.; 1997. p. 88–100.
Gaffney S, Smyth P. Trajectory clustering with mixtures of regression models. Proc. 5th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining; 1999. p. 63–72.
Hadjieleftheriou M, Kollios G, Gunopulos D, Tsotras VJ. On-line discovery of dense areas in spatio-temporal databases. Proc. 8th Int. Symp. Advances in Spatial and Temporal Databases; 2003. p. 306–324.
Han J, Kamber M. Data mining: concepts and techniques. San Francisco: Morgan Kaufmann; 2000.
Kalnis P, Mamoulis N, Bakiras S. On discovering moving clusters in spatio-temporal data. Proc. 9th Int. Symp. Advances in Spatial and Temporal Databases; 2005. p. 364–381.
Lee J-G, Han J, Whang K-Y. Trajectory clustering: a partition-and-group framework. Proc. ACM SIGMOD Int. Conf. on Management of Data; 2007. p. 593–604.
Mamoulis N, Cao H, Kollios G, Hadjieleftheriou M, Tao Y, Cheung D.W. Mining, indexing, and querying historical spatio-temporal data. Proc. 10th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining; 2004. p. 236–245.
Tao Y, Faloutsos C, Papadias D, Liu B. Prediction and indexing of moving objects with unknown motion patterns. Proc. ACM SIGMOD Int. Conf. on Management of Data; 2004. p. 611–622.
Tsoukatos I, Gunopulos D. Efficient mining of spatio-temporal patterns. Proc. 7th Int. Symp. Advances in Spatial and Temporal Databases; 2001. p. 425–442.
Vlachos M, Gunopulos D, Kollios G. Discovering similar multidimensional trajectories. Proc. 18th Int. Conf. on Data Engineering; 2002. p. 673–684.
Zaki MJ. Spade: an efficient algorithm for mining frequent sequences. Mach Learn. 2001;42(1/2):31–60.
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Mamoulis, N. (2014). Spatio-temporal Data Mining. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_361-2
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DOI: https://doi.org/10.1007/978-1-4899-7993-3_361-2
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