Scientific Programming

Scientific Programming Techniques and Algorithms for Data-Intensive Engineering Environments


Publishing date
01 Jul 2018
Status
Published
Submission deadline
23 Feb 2018

1Instituto Tecnológico de Orizaba, Orizaba, Mexico

2Centro de Investigación en Matemáticas (CIMAT), Guanajuato, Mexico

3Carlos III University of Madrid, Madrid, Spain


Scientific Programming Techniques and Algorithms for Data-Intensive Engineering Environments

Description

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

Articles

  • Special Issue
  • - Volume 2018
  • - Article ID 1351239
  • - Editorial

Scientific Programming Techniques and Algorithms for Data-Intensive Engineering Environments

Giner Alor-Hernández | Jezreel Mejía-Miranda | José María Álvarez-Rodríguez
  • Special Issue
  • - Volume 2018
  • - Article ID 8467413
  • - Review Article

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Jose María Álvarez-Rodríguez | Giner Alor-Hernández | Jezreel Mejía-Miranda
  • Special Issue
  • - Volume 2018
  • - Article ID 4304017
  • - Research Article

Analysis of Medical Opinions about the Nonrealization of Autopsies in a Mexican Hospital Using Association Rules and Bayesian Networks

Elayne Rubio Delgado | Lisbeth Rodríguez-Mazahua | ... | Asdrúbal López-Chau
  • Special Issue
  • - Volume 2017
  • - Article ID 1496104
  • - Research Article

Scalable Parallel Distributed Coprocessor System for Graph Searching Problems with Massive Data

Wanrong Huang | Xiaodong Yi | ... | Hengzhu Liu
  • Special Issue
  • - Volume 2017
  • - Article ID 7831897
  • - Research Article

Semantic Annotation of Unstructured Documents Using Concepts Similarity

Fernando Pech | Alicia Martinez | ... | Yasmin Hernandez
  • Special Issue
  • - Volume 2017
  • - Article ID 1083062
  • - Research Article

Design and Solution of a Surrogate Model for Portfolio Optimization Based on Project Ranking

Eduardo Fernandez | Claudia Gómez-Santillán | ... | Shulamith Bastiani
  • Special Issue
  • - Volume 2017
  • - Article ID 6459582
  • - Research Article

Applying Softcomputing for Copper Recovery in Leaching Process

Claudio Leiva | Víctor Flores | ... | Claudio Acuña
  • Special Issue
  • - Volume 2017
  • - Article ID 8131390
  • - Research Article

A Heterogeneous System Based on Latent Semantic Analysis Using GPU and Multi-CPU

Gabriel A. León-Paredes | Liliana I. Barbosa-Santillán | Juan J. Sánchez-Escobar
  • Special Issue
  • - Volume 2017
  • - Article ID 1329281
  • - Research Article

Sentiment Analysis in Spanish for Improvement of Products and Services: A Deep Learning Approach

Mario Andrés Paredes-Valverde | Ricardo Colomo-Palacios | ... | Rafael Valencia-García
Scientific Programming
 Journal metrics
Acceptance rate27%
Submission to final decision65 days
Acceptance to publication33 days
CiteScore2.300
Impact Factor0.963
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