Prov-Dominoes: An approach for knowledge discovery from provenance data

Provenance has become increasingly relevant to understanding, auditing, and reproducing computational tasks. The provenance analysis processes can often be overwhelming to the user due to the large volume of data, the multiple relationships among data, and the implicit information buried in the data. Existing provenance analysis tools use either visual exploration (which is overwhelming for large provenance graphs) or do not support the exploration of implicit provenance data, such as the inferences of the PROV Data Model Constraints. To fill in this gap, we introduce Prov-Dominoes, a tool designed to interactively enable knowledge discovery on provenance data. Prov-Dominoes promotes the provenance relationships among entities, activities, and agents into first-class elements represented by domino tiles. It allows users to combine and compose such domino tiles visually and interactively, using GPU. The benefits of Prov-Dominoes are three-fold: first, it uses matrices to display provenance data, which is more compact than graphs; second, it allows users to easily explore implicit information; third, it is capable of efficiently processing large datasets using GPUs. We evaluated Prov-Dominoes over distinct case studies, allowing the observation of Prov-Dominoes in action. We also evaluated the performance of sequential combinations executed in Prov-Dominoes when dealing with provenance data with thousands of relations, contrasting their executions in GPU and CPU. The results showed that, for a large dataset, GPU was more than a hundred times faster than CPU. •A novel approach for uncovering implicit information among provenance data.•An interactive tool for provenance exploratory analysis.•Matrix and eigenvector centrality visualizations for provenance data.•Environment for employing W3C PROV inferences for provenance data.•Exploration of large provenance data with GPU.