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Scientific Programming Techniques and Algorithms for Data-Intensive Engineering Environments
| dc.contributor.author | Alor-Hernández, Giner | |
| dc.contributor.author | Mejia-Miranda, Jezreel | |
| dc.contributor.author | Alvarez-Rodríguez, José Maria | |
| dc.date.accessioned | 2021-07-07T16:18:48Z | |
| dc.date.available | 2021-07-07T16:18:48Z | |
| dc.date.issued | 2018-02-19 | |
| dc.identifier.issn | 1058-9244 | |
| dc.identifier.other | https://doi.org/10.1155/2018/1351239 | |
| dc.identifier.uri | http://repositorios.orizaba.tecnm.mx:8080/xmlui/handle/123456789/497 | |
| dc.description | In recent years, the development, advancement, and use of Information and Communications Technology (ICT) have had a major impact on the operation, structure, and strategy of organizations around the world. Today, it is unthinkable to conceive an organization without the use of ICT, because it allows for the reduction of communication costs and operation while increasing flexibility, interactivity, performance, and productivity. As a result, digital technology fueled by ICT and ICT is currently embedded in any task, activity, and process that is done in any organization or even our daily life activities. This new digital age also implies that science, engineering, and business environments need to reshape their strategies and underlying technology to become a key player of the Industrial Revolution 4.0. Engineering methods such as requirements engineering, systems modeling, complex network analysis, or simulation are currently applied to support the development of critical systems and decision-making processes in operational environments. As an example, cyberphysical systems featured by mechanical, electrical, and software components are a major challenge for the industry in which new and integrated science and engineering techniques are required to develop and operate these systems in a collaborative data-intensive environment. 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 intelligent and federated programming platforms to support continuous and collaborative engineering. More specifically, the availability of huge amounts of data that are continuously generated by persons, tools, sensors, and any other smart connected device 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 to support the proper development and operation of complex systems. In this context, the evolution and extension of engineering methods through scientific programming techniques in data-intensive environments are expected to take advantage of innovative algorithms implemented using different programming paradigms and execution platforms. 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 was 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. | es |
| dc.description.abstract | In recent years, the development, advancement, and use of Information and Communications Technology (ICT) have had a major impact on the operation, structure, and strategy of organizations around the world. Today, it is unthinkable to conceive an organization without the use of ICT, because it allows for the reduction of communication costs and operation while increasing flexibility, interactivity, performance, and productivity. As a result, digital technology fueled by ICT and ICT is currently embedded in any task, activity, and process that is done in any organization or even our daily life activities. This new digital age also implies that science, engineering, and business environments need to reshape their strategies and underlying technology to become a key player of the Industrial Revolution 4.0. Engineering methods such as requirements engineering, systems modeling, complex network analysis, or simulation are currently applied to support the development of critical systems and decision-making processes in operational environments. As an example, cyberphysical systems featured by mechanical, electrical, and software components are a major challenge for the industry in which new and integrated science and engineering techniques are required to develop and operate these systems in a collaborative data-intensive environment. 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 intelligent and federated programming platforms to support continuous and collaborative engineering. More specifically, the availability of huge amounts of data that are continuously generated by persons, tools, sensors, and any other smart connected device 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 to support the proper development and operation of complex systems. In this context, the evolution and extension of engineering methods through scientific programming techniques in data-intensive environments are expected to take advantage of innovative algorithms implemented using different programming paradigms and execution platforms. 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 was 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. | es |
| dc.language.iso | en_US | es |
| dc.publisher | Hindawi Publishing | es |
| dc.relation.ispartofseries | Scientific Programming; | |
| dc.subject | Scientific Porgramming | es |
| dc.title | Scientific Programming Techniques and Algorithms for Data-Intensive Engineering Environments | es |
| dc.type | Article | es |
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Artículos (MSC) [39]