Rodrigo Almeida, Waldeyr M. C. Silva, Klayton Castro, Aleteia P. F. Araujo, M. E. Walter, Sérgio Lifschitz, M. Holanda
{"title":"Managing data provenance for bioinformatics workflows using AProvBio","authors":"Rodrigo Almeida, Waldeyr M. C. Silva, Klayton Castro, Aleteia P. F. Araujo, M. E. Walter, Sérgio Lifschitz, M. Holanda","doi":"10.1504/IJCBDD.2019.10021271","DOIUrl":null,"url":null,"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.","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"23 1","pages":"153-170"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Comput. Biol. Drug Des.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/IJCBDD.2019.10021271","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
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.
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