Visionary: a framework for analysis and visualization of provenance data

Provenance is recognized as a central challenge to establish the reliability and provide security in computational systems. In scientific workflows, provenance is considered essential to support experiments’ reproducibility, interpretation of results, and problem diagnosis. We consider that these requirements can also be used in new application domains, such as software processes and IoT. However, for a better understanding and use of provenance data, efficient and user-friendly mechanisms are needed. Ontology, complex networks, and software visualization can help in this process by generating new data insights and strategic information for decision-making. This paper presents the Visionary framework, designed to assist in the understanding and use of provenance data through ontologies, complex network analysis, and software visualization techniques. The framework captures the provenance data and generates new information using ontologies and structural analysis of the provenance graph. The visualization presents and highlights inferences and results obtained with the data analysis. Visionary is an application domain-free framework adapted to any system that uses the PROV provenance model. Evaluations were carried out, and some evidence was found that the framework assists in the understanding and analysis of provenance data when decision-making is needed.