Models and data engineering

Ladjel Bellatreche

doi:10.1016/j.future.2016.11.019

Models and data engineering

2017-05-01

0

PRE

AI

George Α. Papadopoulos

DOI：10.1016/j.future.2016.11.019

原文链接

原文求助

摘要

En 中文

Data and models are two well established communities that are continuously contributing in new challenges in different research domains including cyber physical systems (Yoo and Shon, 2016 [1]), cloud computing (Jararweh et al., 2016 [2]), service oriented applications, social networks (Yahyaoui et al., 2013 [3]), big data (with its five Vs characteristics: Volume, Variety, Velocity, Veracity and Value) (Hsu, 2014), etc. The success story of data and models communities is mainly based on the availability of foundations relying on formal methods (Hassan et al., 2016), modelling methods (Ameur et al., 2014), storage systems and platforms (Liu et al., 2015 [7]), advanced optimization structures, benchmarking, scalability, etc. These foundations are usually associated with tools and commercial and academic systems. The selected papers for this special issue address a variety of topics and concerns in models and data fields, including advanced databases, engagement systems, embedded and complex systems, etc. (C) 2016 Elsevier B.V. All rights reserved.

Keyword:

Modelling

Ontologies

Formal methods

Engagement systems

Data warehouses

Clustering

Model and data stores

Models and data engineering

摘要

AI总结

期刊

机构