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The Model-Driven Architecture for the Trajectory Data Warehouse Modeling

The Model-Driven Architecture for the Trajectory Data Warehouse Modeling

Noura Azaiez, Jalel Akaichi
Copyright: © 2020 |Volume: 16 |Issue: 4 |Pages: 18
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781799805007|DOI: 10.4018/IJDWM.2020100102
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MLA

Azaiez, Noura, and Jalel Akaichi. "The Model-Driven Architecture for the Trajectory Data Warehouse Modeling." IJDWM vol.16, no.4 2020: pp.26-43. http://doi.org/10.4018/IJDWM.2020100102

APA

Azaiez, N. & Akaichi, J. (2020). The Model-Driven Architecture for the Trajectory Data Warehouse Modeling. International Journal of Data Warehousing and Mining (IJDWM), 16(4), 26-43. http://doi.org/10.4018/IJDWM.2020100102

Chicago

Azaiez, Noura, and Jalel Akaichi. "The Model-Driven Architecture for the Trajectory Data Warehouse Modeling," International Journal of Data Warehousing and Mining (IJDWM) 16, no.4: 26-43. http://doi.org/10.4018/IJDWM.2020100102

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Abstract

Business Intelligence includes the concept of data warehousing to support decision making. As the ETL process presents the core of the warehousing technology, it is responsible for pulling data out of the source systems and placing it into a data warehouse. Given the technology development in the field of geographical information systems, pervasive systems, and the positioning systems, the traditional warehouse features become unable to handle the mobility aspect integrated in the warehousing chain. Therefore, the trajectory or the mobility data gathered from the mobile object movements have to be managed through what is called the trajectory ELT. For this purpose, the authors emphasize the power of the model-driven architecture approach to achieve the whole transformation task, in this case transforming trajectory data source model that describes the resulting trajectories into trajectory data mart models. The authors illustrate the proposed approach with an epilepsy patient state case study.

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