Abstract
In this chapter, we will focus on the spatiotemporal object modeling and put special attention on the moving objects with extended geometric representations. Our spatiotemporal frequent pattern mining algorithms primarily make use of region trajectories whose polygon-based region representations continuously evolve over time. In the rest of this chapter, we will firstly introduce the conceptual modeling of spatiotemporal trajectories and moving objects. Then, we will present the evolving region trajectories and spatiotemporal event instances which are the base data types in our mining schema.
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Aydin, B., Akkineni, V., Angryk, R.: Modeling and indexing spatiotemporal trajectory data in non-relational databases. In: Managing Big Data in Cloud Computing Environments, pp. 133–162. IGI Global (2016). https://doi.org/10.4018/978-1-4666-9834-5.ch006
Giannotti, F., Nanni, M., Pinelli, F., Pedreschi, D.: Trajectory pattern mining. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, California, USA, August 12–15, 2007, pp. 330–339 (2007)
Güting, R.H., Böhlen, M.H., Erwig, M., Jensen, C.S., Lorentzos, N.A., Schneider, M., Vazirgiannis, M.: A foundation for representing and querying moving objects. ACM Trans. Database Syst. 25(1), 1–42 (2000). https://doi.org/10.1145/352958.352963. URL http://doi.acm.org/10.1145/352958.352963
Güting, R.H., Valdés, F., Damiani, M.L.: Symbolic trajectories. ACM Trans. Spatial Algorithms and Systems 1(2), 7:1–7:51 (2015). https://doi.org/10.1145/2786756. URL http://doi.acm.org/10.1145/2786756
Jiang, Z., Shekhar, S.: Spatial and spatiotemporal big data science. In: Spatial Big Data Science, pp. 15–44. Springer (2017)
Lema, J.A.C., Forlizzi, L., Güting, R.H., Nardelli, E., Schneider, M.: Algorithms for moving objects databases. Comput. J. 46(6), 680–712 (2003). https://doi.org/10.1093/comjnl/46.6.680
Marketos, G., Theodoridis, Y.: Mobility data warehousing and mining. In: Proceedings of the VLDB 2009 PhD Workshop. Co-located with the 35th International Conference on Very Large Data Bases (VLDB 2009). Lyon, France, August 24, 2009 (2009). URL http://www.vldb.org/pvldb/2/vldb09-1063.pdf
du Mouza, C., Rigaux, P.: Mobility patterns. GeoInformatica 9(4), 297–319 (2005). https://doi.org/10.1007/s10707-005-4574-9. URL http://dx.doi.org/10.1007/s10707-005-4574-9
Parent, C., Spaccapietra, S., Renso, C., Andrienko, G.L., Andrienko, N.V., Bogorny, V., Damiani, M.L., Gkoulalas-Divanis, A., de Macêdo, J.A.F., Pelekis, N., Theodoridis, Y., Yan, Z.: Semantic trajectories modeling and analysis. ACM Comput. Surv. 45(4), 42:1–42:32 (2013). https://doi.org/10.1145/2501654.2501656. URL http://doi.acm.org/10.1145/2501654.2501656
Spaccapietra, S., Parent, C., Damiani, M.L., de Macêdo, J.A.F., Porto, F., Vangenot, C.: A conceptual view on trajectories. Data Knowl. Eng. 65(1), 126–146 (2008). https://doi.org/10.1016/j.datak.2007.10.008
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Aydin, B., Angryk, R.A. (2018). Modeling Spatiotemporal Trajectories. In: Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-99873-2_2
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