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Mobility Data Warehouses

1
Department of Information Engineering, Instituto Tecnológico de Buenos Aires, Lavardén 315, C1437FBG Ciudad Autónoma de Buenos Aires, Argentina
2
Department of Computer & Decision Engineering (CoDE), CP 165/15 Université Libre de Bruxelles, Avenue F. D. Roosevelt 50, B-1050 Brussels, Belgium
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(4), 170; https://doi.org/10.3390/ijgi8040170
Received: 9 January 2019 / Revised: 12 March 2019 / Accepted: 29 March 2019 / Published: 2 April 2019
(This article belongs to the Special Issue Distributed and Parallel Architectures for Spatial Data)
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Abstract

The interest in mobility data analysis has grown dramatically with the wide availability of devices that track the position of moving objects. Mobility analysis can be applied, for example, to analyze traffic flows. To support mobility analysis, trajectory data warehousing techniques can be used. Trajectory data warehouses typically include, as measures, segments of trajectories, linked to spatial and non-spatial contextual dimensions. This paper goes beyond this concept, by including, as measures, the trajectories of moving objects at any point in time. In this way, online analytical processing (OLAP) queries, typically including aggregation, can be combined with moving object queries, to express queries like “List the total number of trucks running at less than 2 km from each other more than 50% of its route in the province of Antwerp” in a concise and elegant way. Existing proposals for trajectory data warehouses do not support queries like this, since they are based on either the segmentation of the trajectories, or a pre-aggregation of measures. The solution presented here is implemented using MobilityDB, a moving object database that extends the PostgresSQL database with temporal data types, allowing seamless integration with relational spatial and non-spatial data. This integration leads to the concept of mobility data warehouses. This paper discusses modeling and querying mobility data warehouses, providing a comprehensive collection of queries implemented using PostgresSQL and PostGIS as database backend, extended with the libraries provided by MobilityDB. View Full-Text
Keywords: mobility; data warehouses; spatiotemporal OLAP; mobility analytics mobility; data warehouses; spatiotemporal OLAP; mobility analytics
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Vaisman, A.; Zimányi, E. Mobility Data Warehouses. ISPRS Int. J. Geo-Inf. 2019, 8, 170.

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