Paper Info
Title
Raising Time Awareness in Model-Driven Engineering: Vision Paper
Abstract
The conviction that big data analytics is a key for the success of modern businesses is growing deeper, and the mobilisation 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.
Year
DOI
Venue
2017
10.1109/MODELS.2017.11
2017 ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems (MODELS)
Keywords
Field
DocType
Model-Driven Engineering,Analytics,Big Data,Temporal Data,Internet of Things
Data science,Data modeling,Systems engineering,Smart grid,Domain knowledge,Unified Modeling Language,Model-driven architecture,Computer science,Temporal database,Analytics,Big data
Conference
ISBN
Citations 
PageRank 
978-1-5386-3493-6
0
0.34
References 
Authors
29
7
Name
Order
Citations
PageRank
Amine Benelallam1747.02
Thomas Hartmann 00012458.08
Ludovic Mouline300.34
François Fouquet411715.16
Johann Bourcier513716.69
Olivier Barais672461.99
Yves Le Traon715514.08