Mostrar el registro sencillo del ítem

dc.contributor.authorAlvarez-Rodríguez, José María
dc.contributor.authorAlor-Hernández, Giner
dc.contributor.authorMejia-Miranda, Jezreel
dc.date.accessioned2022-06-25T22:28:44Z
dc.date.available2022-06-25T22:28:44Z
dc.date.issued2018-10-30
dc.identifier.citationJose María Alvarez-Rodrıguez, Giner Alor-Hernandez, Jezreel Mejia-Miranda. Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments. Scientific Programming. Hindawi Publishing.. pp. 1-21. ISSN: 1058-9244. 2017. https://doi.org/10.1155/2018/8467413es
dc.identifier.issn1058-9244
dc.identifier.urihttp://repositorios.orizaba.tecnm.mx:8080/xmlui/handle/123456789/546
dc.descriptionThe present paper introduces and reviews existing technology and research works in the field of scientific programming methods and techniques in data-intensive engineering environments. More specifically, this survey aims to collect those relevant approaches that have faced the challenge of delivering more advanced and intelligent methods taking advantage of the existing large datasets. Although existing tools and techniques have demonstrated their ability to manage complex engineering processes for the development and operation of safety-critical systems, there is an emerging need to know how existing computational science methods will behave to manage large amounts of data. That is why, authors review both existing open issues in the context of engineering with special focus on scientific programming techniques and hybrid approaches. 1193 journal papers have been found as the representative in these areas screening 935 to finally make a full review of 122. Afterwards, a comprehensive mapping between techniques and engineering and nonengineering domains has been conducted to classify and perform a meta-analysis of the current state of the art. As the main result of this work, a set of 10 challenges for future data-intensive engineering environments have been outlined.es
dc.description.abstractThe present paper introduces and reviews existing technology and research works in the field of scientific programming methods and techniques in data-intensive engineering environments. More specifically, this survey aims to collect those relevant approaches that have faced the challenge of delivering more advanced and intelligent methods taking advantage of the existing large datasets. Although existing tools and techniques have demonstrated their ability to manage complex engineering processes for the development and operation of safety-critical systems, there is an emerging need to know how existing computational science methods will behave to manage large amounts of data. That is why, authors review both existing open issues in the context of engineering with special focus on scientific programming techniques and hybrid approaches. 1193 journal papers have been found as the representative in these areas screening 935 to finally make a full review of 122. Afterwards, a comprehensive mapping between techniques and engineering and nonengineering domains has been conducted to classify and perform a meta-analysis of the current state of the art. As the main result of this work, a set of 10 challenges for future data-intensive engineering environments have been outlined.es
dc.description.sponsorshipCONACYT, TECNM, PRODEPes
dc.language.isoen_USes
dc.publisherHindawi Publishinges
dc.relation.ispartofseriesScientific Programming;
dc.subjectData-Intensive Engineering Environmentses
dc.subjectScientific Programming Techniqueses
dc.subjectSurveyes
dc.titleSurvey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environmentses
dc.typeArticlees


Ficheros en el ítem

Thumbnail

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem