Managing data provenance for bioinformatics workflows using AProvBio. (11th May 2019)

• Record Type: Journal Article
• Authors: Almeida, Rodrigo; Silva, Waldeyr Mendes Cordeiro Da; Castro, Klayton; Araújo;, Aletéia Patricia Favacho De; Walter, Maria Emília Machado Telles; Lifschitz, Sergio; Holanda, Maristela
• Is Part Of: International journal of computational biology and drug design. Volume 12:Number 2(2019)
• Page Start: 153
• Page End: 170
• Publication Date: 2019-05-11
• Subjects: bioinformatics -- scientific workflows -- data provenance -- PROV-DM -- graph database; Computational biology -- Periodicals; Drugs -- Design -- Periodicals; 570.285
• Languages: English
• ISSNs: 1756-0756

Abstract

Scientific experiments in bioinformatics are often executed as computational workflows. Data provenance involves documenting the history, and the paths of the input data, from the beginning to the end of an experiment. AProvBio is an architecture that enables the capture and storage of data provenance for bioinformatics workflows using the PROV-DM standard model. AProvBio works with three types of data provenance: prospect, retrospect, and the user-defined type. Given how graphs conveniently express PROV-DM, we have designed and implemented a simulator for storing the data provenance in a graph database system. This paper presents details and implementation aspects of our architecture, and an evaluation of AProvBio through the carrying out of two real case scenarios.