Abstract
The ability to query vast amount of historical data for statistical analysis and reporting is provided by Data Warehouses. They facilitate Business Intelligence for effective decision-making significantly. In recent years, great progress has been made in movement monitoring devices, such as smart phones and GPSs. The storing and managing of spatio-temporal data related to the trajectories of moving objects in a data warehouse is called Trajectory Data Warehouse (TDW). The relational approach is adopted widely for the logical representation of TDWs, since it is based on the classic database approach where data representation and processing are handled on structured data. In this paper, the key idea is to consider different logical relational TDW models, i.e. flat, segment and complex, which are compared and evaluated. The study is based on a novel classification of OLAP queries, the cardinality of facts and the resolution of each trajectory in segments. Real data provided by agricultural autonomous robots is used, where experiments on size and time performances are conducted and discussed.
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Notes
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Due to pages limit, this figure is presented in the Appendix.
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Due to pages limit, this Table is presented in the Appendix.
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Due to pages limit, this Table is presented in the Appendix.
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This work is supported by the French National Research Agency project IDEX-ISITE initiative 16-IDEX-0001 (CAP 20-25).
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7 Appendix
7 Appendix
Logical multidimensional examples: a) flat, b) complex, c) segment
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Oikonomou, K., Garani, G., Bimonte, S., Wrembel, R. (2022). What Logical Model Is Suitable for Relational Trajectory Data Warehouses?. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2022. Lecture Notes in Computer Science, vol 13426. Springer, Cham. https://doi.org/10.1007/978-3-031-12423-5_30
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