Abstract
Exploring mobility data already collected and stored in efficient database systems is the next step of mobility data management. Historical data ‘hide’ a treasure of ‘buried’ knowledge that ‘asks’ for mining. To do so, the typical Knowledge Discovery in Data (KDD) process typically includes the organization of historical information in a Data Warehouse (DW), a first level of analysis exploiting on data cubes build upon the DW, according to a multi-dimensional model, and, then, a deeper look into the data in order to extract models and patterns that data obey or follow, using data mining techniques. In this chapter, we provide the preparatory actions in order for data mining to follow in the next chapter. In particular, we present DW approaches for mobility, especially for trajectory data (Sect. 6.1), discuss about the kind of multi-dimensional analysis that is suitable for mobility data and the challenges that arise due to its peculiarity (Sect. 6.2), and present a methodology for progressive, interactive analysis that is useful to mobility data scientists (Sect. 6.3). Finally, we provide sound definitions for trajectory similarity, which is a key component of whatever analysis to be made with trajectory databases (Sect. 6.4).
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Pelekis, N., Theodoridis, Y. (2014). Preparing for Mobility Data Exploration. In: Mobility Data Management and Exploration. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0392-4_6
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DOI: https://doi.org/10.1007/978-1-4939-0392-4_6
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