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Authors: Noura Azaiez 1 and Jalel Akaichi 2

Affiliations: 1 University of Tunis, Tunisia ; 2 King Khalid University, Saudi Arabia

ISBN: 978-989-758-210-3

Keyword(s): Trajectory ELT Processes, Extraction, Loading, Transformation, Trajectory Construction, Trajectory Data Source Model, Trajectory Data Mart Model, Model Driven Architecture.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Process Management ; e-Business ; Enterprise Engineering ; Enterprise Information Systems ; Knowledge Management and Information Sharing ; Knowledge-Based Systems ; Languages, Tools and Architectures ; Methodologies, Processes and Platforms ; Model Transformation ; Model Transformations and Generative Approaches ; Model-Driven Architecture ; Model-Driven Software Development ; Models ; Paradigm Trends ; Software Engineering ; Symbolic Systems

Abstract: Business Intelligence is often described as a set of techniques serving the transformation of raw data into meaningful information for business analysis purposes. Thanks to the technology development in the realm of Geographical Information Systems, the so-called trajectory data were appeared. Analysing these raw trajectory data coming from the movements of mobile objects requires their transformation into decisional data. Usually, the Extraction-Transformation-Loading (ETL) process ensures this task. However, it seems inadequate to support trajectory data. Integrating the trajectory aspects gives the birth of Trajectory ETL process (T-ETL). Unfortunately, this is not enough. In fact, the business analysis main purpose is to minimize costs and time consuming. Thus, we propose to swap the T-ETL tasks scheduling: instead of transforming the data before they are written, the Trajectory Extraction, Loading and Transformation (T-ELT) process leverages the target system to achieve the trans formation task. In this paper, we rely on a set of powerful mechanisms to handle the complexity of each T-ELT task. Wherefore, an algorithm is dedicated to ensure the transformation of raw mobile object positions into trajectories and from there we highlight the power of the Model-driven Architecture approach to transform the resulting trajectories into analytical data in order to perform the Business Intelligence goal. (More)

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Paper citation in several formats:
Azaiez, N. and Akaichi, J. (2017). Override Traditional Decision Support Systems - How Trajectory ELT Processes Modeling Improves Decision Making?.In Proceedings of the 5th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD, ISBN 978-989-758-210-3, pages 550-555. DOI: 10.5220/0006269605500555

@conference{modelsward17,
author={Noura Azaiez. and Jalel Akaichi.},
title={Override Traditional Decision Support Systems - How Trajectory ELT Processes Modeling Improves Decision Making?},
booktitle={Proceedings of the 5th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD,},
year={2017},
pages={550-555},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006269605500555},
isbn={978-989-758-210-3},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD,
TI - Override Traditional Decision Support Systems - How Trajectory ELT Processes Modeling Improves Decision Making?
SN - 978-989-758-210-3
AU - Azaiez, N.
AU - Akaichi, J.
PY - 2017
SP - 550
EP - 555
DO - 10.5220/0006269605500555

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