Prov Viewer: A Graph-Based Visualization Tool for Interactive Exploration of Provenance Data

Kohwalter, Troy C.; Oliveira, Thiago; Freire, Juliana; Clua, Esteban; Murta, Leonardo

doi:10.1007/978-3-319-40593-3_6

Cited by 22 publications

(12 citation statements)

References 14 publications

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“…As we mentioned before, some approaches export provenance as interoperable files (e.g., OPM, PROV) for visualization in external tools [90,124]. However, since provenance can be tight to a domain or not exported to interoperable files, some approaches that collect provenance offer their own internal visualization mechanisms [1,34,62,70,75,87,98].…”

Section: Visualizationmentioning

confidence: 99%

“…Most approaches visualize provenance either as logs [51] or as directed graphs [1,34,62,70,75,87,98]. Such graphs present data transformations, data communication between activities, or activities sequence.…”

Section: Visualizationmentioning

confidence: 99%

“…Provenance clustering combines similar nodes and edges in the provenance graph. It can be performed manually [34,62] or automatically [70,98]. Manual approaches require users to select which nodes they want to combine into a single node.…”

Section: Visualizationmentioning

confidence: 99%

“…Automatic approaches use similarity measures for clustering. The similarity measures might consider provenance sequencing [70] or not [98]. Approaches that do not consider sequencing can break acyclic constraints of provenance during summarizations.…”

Section: Visualizationmentioning

confidence: 99%

“…Some query languages are distributed with provenance browsers that support provenance visualization [6]. Alternatively, it is possible to filter provenance with simple predefined filters, such as temporal filters for selecting provenance data produced in a specific time range [70].…”

Section: Visualizationmentioning

confidence: 99%

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A Survey on Collecting, Managing, and Analyzing Provenance from Scripts

et al. 2019

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Scripts are widely used to design and run scientific experiments. Scripting languages are easy to learn and use, and they allow complex tasks to be specified and executed in fewer steps than with traditional programming languages. However, they also have important limitations for reproducibility and data management. As experiments are iteratively refined, it is challenging to reason about each experiment run (or trial), to keep track of the association between trials and experiment instances as well as the differences across trials, and to connect results to specific input data and parameters. Approaches have been proposed that address these limitations by collecting, managing, and analyzing the provenance of scripts. In this article, we survey the state of the art in provenance for scripts. We have identified the approaches by following an exhaustive protocol of forward and backward literature snowballing. Based on a detailed study, we propose a taxonomy and classify the approaches using this taxonomy.

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Section: Visualizationmentioning

confidence: 99%