Scientific provenance metadata capture and management using Semantic Web

Gaspar, Wander Antunes; Braga, Regina; Campos, Fernanda; David, José; Ornelas, Tatiane

doi:10.1504/ijmso.2015.070829

Cited by 3 publications

(5 citation statements)

References 31 publications

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“…SciProMiner works with any SWMS that can execute Web services, as detailed in Section 4.1. Besides, the suitability of SciProvMiner architecture for the capturing and management of data provenance and processes in the context of distributed scientific experiments has been validated in Gaspar et al Considering that SciProvMiner extends the architecture defined in Gaspar et al, the goal of this study was to assess the functionalities of OPMO‐e and SciProvMiner to capture prospective and retrospective provenance data. As data sources, we used direct observation and data analysis.…”

Section: Discussionmentioning

confidence: 99%

“…SciProvMiner is an extension of SciProv architecture, which proposes a provenance data model whose purpose is to interact with the SWMS in a collaborative environment. It is worth mentioning that SciProvMiner has the same characteristics presented by SciProv and extends its architecture by adding the ability to capture prospective provenance and the possibility of using completeness and inference rules defined in the OPM documentation.…”

Section: Discussionmentioning

confidence: 99%

“…Below, we highlight the main contributions of SciProvMiner, considering SciProv: Improvement in the instrumentalization mechanism of scientific workflows to optimize the performance of capture and persistence of provenance metadata and decrease the workload of the scientist in the workflow instrumentalization. Adherence to the OPM abstract model, thus considering the latest version 1.1. SciProvMiner includes all the features available in this version. Adaptation of the architecture to enable the capture, management, and query of prospective and retrospective data provenance, enabling that issues related to the specification of the workflow, which cannot be answered using only retrospective provenance, can be answered by SciProvMiner. Specification of completeness rules and validity of inferences in the OPM, modelled using ontologies.…”

Section: Sciprovminermentioning

confidence: 99%

“…The architecture of SciProvMiner is an evolution of the architecture of SciProv. 20 It adds the ability to capture prospective provenance and the possibility of using the completeness and inference rules defined in the OPM documentation. To incorporate such functionalities, an extension of the OPM that supports capture prospective provenance was specified.…”

Section: Sciprovminermentioning

confidence: 99%

“…The architecture of SciProvMiner is an evolution of the architecture of SciProv . It adds the ability to capture prospective provenance and the possibility of using the completeness and inference rules defined in the OPM documentation.…”

Section: Sciprovminermentioning

confidence: 99%

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Provenance data discovery through Semantic Web resources

Ornelas

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et al. 2017

Concurrency and Computation

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Summary Providing historical information to deal with knowledge loss about a scientific experiment has been the focus of some several researches. However, computational support for large‐scale scientific experiments is still incipient and is considered one of e‐science's greatest challenges. In this vein, providing provenance information to a scientist is part of these challenges. Provenance information helps to assure the reliability and reproducibility of experiments. Therefore, this work has as its main objective to present a new ontology—Open Provenance Model Ontology‐e (OPMO‐e)—which is part of an architecture, named SciProvMiner. Open Provenance Model Ontology‐e is a new ontology that encompasses both prospective and retrospective provenance. SciProvMiner captures the provenance and implements all inference and completeness rules defined by Open Provenance Model to provide provenance information beyond those already established. We hypothesize that the capture and management of provenance data will provide scientists with implicit strategical information about the experiment through OPMO‐e. Case studies were carried out to evaluate OPMO‐e use considering SciProvMiner. The obtained results revealed that the use of the proposed ontology and SciProvMiner can enhance scientist's knowledge about an experiment.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%