Scientific Programing Techniques and Algorithms for Data-Intensive Engineering Environments
Call for Papers

The notion of “Industry 4.0” has emerged to lead industry to a digital environment in which the adaptation of existing science and engineering methods (e.g., requirements engineering, systems modeling, and complex network analysis or simulation) is required to reshape their business strategy and underlying technology. Thus, the industry will be able to create advanced and collaborative engineering environments for building and operating more and more complex and connected systems, Cyberphysical Systems (CPS).

Both the development processes and the operational environments of complex systems need the application of scientific and engineering methods to fulfill the management of new multidisciplinary, data-intensive, and software-centric environments. Programming paradigms such as functional, symbolic, logic, linear, or reactive programming in conjunction with development platforms are considered a cornerstone for the proper development of collaborative and federated engineering platforms.

More specifically, the availability of huge amounts of data requires new architectures to address the challenge of solving complex problems such as pattern identification, process optimization, discovery of interactions, knowledge inference, execution of large simulations, or machine cooperation. This situation implies the rethinking and application of innovative scientific programming techniques for numerical, scientific, and engineering computation on top of well-defined hardware and software architectures.

The conjunction of scientific programming techniques and engineering techniques will support and enhance existing development and production environments to provide high-quality, economical, reliable, and efficient data-centric software products and services. This advance in the field of scientific programming methods will become a key enabler for the next wave of software systems and engineering.

Therefore, the main objective of this special issue is to collect and consolidate innovative and high-quality research contributions regarding scientific programing techniques and algorithms applied to the enhancement and improvement of engineering methods to develop real and sustainable data-intensive science and engineering environments. This special issue aims to provide insights into the recent advances in these topics by soliciting original scientific contributions in the form of theoretical foundations, models, experimental research, surveys, and case studies for scientific programing techniques and algorithms in data-intensive environments.

Potential topics include but are not limited to the following:

New scientific programming techniques and algorithms for empowering data science and engineering
Scientific programming algorithms, methods, and languages for modeling and simulation of complex engineering problems
Scientific programming algorithms, languages, methods, and execution platforms for knowledge representation, inference, and reasoning
Scientific programming techniques, algorithms, and methods for large data processing in science and engineering
Scientific programming methods and models for data-driven engineering
Scientific programming methods for data-based decision support systems applied to engineering methods
Data-intensive scientific programming methods and tools for testing, simulation, verification and validation, maintenance, and evolution in engineering
Performance evaluation of algorithms and scientific programming techniques
Authors can submit their manuscripts through the Manuscript Tracking System at http://mts.hindawi.com/submit/journals/sp/ppps/.

Manuscript Due Friday, 5 May 2017
First Round of Reviews Friday, 28 July 2017
Publication Date Friday, 22 September 2017

Lead Guest Editor
Giner Alor-Hernandez, Instituto Tecnológico de Orizaba, Orizaba, Mexico

Guest Editors
Jezreel Mejia-Miranda, Centro de Investigación en Matemáticas (CIMAT), Guanajuato, Mexico
José María Álvarez-Rodríguez, Carlos III University of Madrid, Madrid, Spain