Raising Time Awareness in Model-Driven Engineering

Abstract : The conviction that big data analytics is a key for the success of modern businesses is growing deeper, and the mo-bilisation of companies into adopting it becomes increasingly important. Big data integration projects enable companies to capture their relevant data, to efficiently store it, turn it into domain knowledge, and finally monetize it. In this context, historical data, also called temporal data, is becoming increasingly available and delivers means to analyse the history of applications, discover temporal patterns, and predict future trends. Despite the fact that most data that today's applications are dealing with is inherently temporal current approaches, methodologies, and environments for developing these applications don't provide sufficient support for handling time. We envision that Model-Driven Engineering (MDE) would be an appropriate ecosystem for a seamless and orthogonal integration of time into domain modelling and processing. In this paper, we investigate the state-of-the-art in MDE techniques and tools in order to identify the missing bricks for raising time-awareness in MDE and outline research directions in this emerging domain.
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Communication dans un congrès
ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems., Sep 2017, Austin, Texas, United States. 〈https://www.cs.utexas.edu/models2017〉
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Soumis le : vendredi 1 septembre 2017 - 16:46:37
Dernière modification le : mardi 21 novembre 2017 - 15:23:52

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Amine Benelallam, Thomas Hartmann, Ludovic Mouline, Francois Fouquet, Johann Bourcier, et al.. Raising Time Awareness in Model-Driven Engineering. ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems., Sep 2017, Austin, Texas, United States. 〈https://www.cs.utexas.edu/models2017〉. 〈hal-01580554〉

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