Abstract
The analysis of provenance data for an experiment is often crucial to understand the achieved results. For long-running experiments or when provenance is captured at a low granularity, this analysis process can be overwhelming to the user due to the large volume of provenance data. In this paper we introduce, Prov Viewer, a provenance visualization tool that enables users to interactively explore provenance data. Among the visualization and exploratory features, we can cite zooming, filtering, and coloring. Moreover, we use of other properties such as shape and size to distinguish visual elements. These exploratory features are linked to the provenance semantics to ease the comprehension process. We also introduce collapsing and filtering strategies, allowing different levels of granularity exploration and analysis. We describe case studies that show how Prov Viewer has been successfully used to explore provenance in different domains, including games and urban data.
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- 1.
Prov Viewer is available at https://github.com/gems-uff/prov-viewer.
- 2.
DataRio: http://data.rio/dataset.
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The authors thank CAPES, CNPq, and FAPERJ for the financial support.
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Kohwalter, T., Oliveira, T., Freire, J., Clua, E., Murta, L. (2016). Prov Viewer: A Graph-Based Visualization Tool for Interactive Exploration of Provenance Data. In: Mattoso, M., Glavic, B. (eds) Provenance and Annotation of Data and Processes. IPAW 2016. Lecture Notes in Computer Science(), vol 9672. Springer, Cham. https://doi.org/10.1007/978-3-319-40593-3_6
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DOI: https://doi.org/10.1007/978-3-319-40593-3_6
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