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Resumo
A provenincia gerada por sistemas de workflow distintos geralmente expressa usando diferentes formatos. Isto no um problema quando cientistas analisam grafos de provenincia isolados, ou quando eles utilizam o mesmo sistema de workflow. Entretanto, quando h necessidade de analisar grafos de provenincia heterogneos de mltiplos sistemas, as solues existentes no fornecem o apoio necessrio. Para resolver este problema, ns propomos uma arquitetura de integrao de provenincia que adota o ProvONE como modelo de integrao e mostramos como as bases de dados de provenincia distintas podem ser convertidas para um esquema global ProvONE. Desta forma, cientistas podem consultar esta base de dados integrada, explorando e interligando provenincia de vrios sistemas e workflows diferentes que podem representar implementaes distintas do mesmo experimento. Para ilustrar a viabilidade da nossa abordagem, ns desenvolvemos mapeamentos conceituais entre bases de dados de provenincia de quatro sistemas de workflow (e-Science Central, SciCumulus, Taverna e VisTrails). Ns fornecemos cartuchos que implementam tais mapeamentos e geramos uma base de dados integrada expressa em fatos Prolog. Ns tambm desenvolvemos regras Prolog que permitem que cientistas consultem a base de dados integrada. Resultados de uma avaliao experimental demonstram a efetividade e eficincia da nossa abordagem.
Palavras-chave: workflow cientfico; provenincia; provenincia prospectiva; provenincia retrospectiva; anlise de provenincia; anlise de provenincia integrada; interoperabilidade de dados de provenincia; PROV; ProvONE.
Abstract
Provenance generated by different workflow systems is generally expressed using different formats. This is not an issue when scientists analyze provenance graphs in isolation, or when they use the same workflow system. However, when they need to analyze heterogeneous provenance graphs from multiple systems, the existing solutions do not provide the required support. To address this problem, we propose a provenance integration architecture that adopts ProvONE as an integration model and show how different provenance databases can be converted to a global ProvONE schema. Scientists can then query this integrated database, exploring and linking provenance across several different workflows that may represent different implementations of the same experiment. To illustrate the feasibility of our approach, we developed conceptual mappings between the provenance databases of four workflow systems (e-Science Central, SciCumulus, Taverna, and VisTrails). We provide cartridges that implement such mappings and generate an integrated provenance database expressed as Prolog facts. We have also developed Prolog rules that enable scientists to query the integrated database. Results of an experimental evaluation demonstrates the effectiveness and efficiency of our approach.
Keywords: scientific workflow; provenance; prospective provenance; retrospective provenance; provenance analysis; integrated provenance analysis; provenance data interoperability; PROV and ProvONE models.
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