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Big Data Smart Socket (BDSS): a system that abstracts data transfer habits from end users.
Watts, Nicholas A; Feltus, Frank A.
Afiliação
  • Watts NA; Clemson Computing & Information Technology.
  • Feltus FA; Clemson University Department of Genetics & Biochemistry, Clemson, SC 29634, USA.
Bioinformatics ; 33(4): 627-628, 2017 02 15.
Article em En | MEDLINE | ID: mdl-27797780
ABSTRACT
Motivation The ability to centralize and store data for long periods on an end user's computational resources is increasingly difficult for many scientific disciplines. For example, genomics data is increasingly large and distributed, and the data needs to be moved into workflow execution sites ranging from lab workstations to the cloud. However, the typical user is not always informed on emerging network technology or the most efficient methods to move and share data. Thus, the user defaults to using inefficient methods for transfer across the commercial internet.

Results:

To accelerate large data transfer, we created a tool called the Big Data Smart Socket (BDSS) that abstracts data transfer methodology from the user. The user provides BDSS with a manifest of datasets stored in a remote storage repository. BDSS then queries a metadata repository for curated data transfer mechanisms and optimal path to move each of the files in the manifest to the site of workflow execution. BDSS functions as a standalone tool or can be directly integrated into a computational workflow such as provided by the Galaxy Project. To demonstrate applicability, we use BDSS within a biological context, although it is applicable to any scientific domain. Availability and Implementation BDSS is available under version 2 of the GNU General Public License at https//github.com/feltus/BDSS . Contact ffeltus@clemson.edu.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Bases de Dados Factuais / Biologia Computacional Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Bases de Dados Factuais / Biologia Computacional Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2017 Tipo de documento: Article