Biblio

Data Provenance has created an emerging requirement for technologies that enable end users to access, evaluate, and act on the provenance of data in recent years. In the era of Big Data, the amount of data created by corporations around the world has grown each year. As an example, both in the Social Media and e-Science domains, data is growing at an unprecedented rate. As the data has grown rapidly, information on the origin and lifecycle of the data has also grown. In turn, this requires technologies that enable the clarification and interpretation of data through the use of data provenance. This study proposes methodologies towards the visualization of W3C-PROV-O Specification compatible provenance data. The visualizations are done by summarization and comparison of the data provenance. We facilitated the testing of these methodologies by providing a prototype, extending an existing open source visualization tool. We discuss the usability of the proposed methodologies with an experimental study; our initial results show that the proposed approach is usable, and its processing overhead is negligible.

Collecting and processing provenance, i.e., information describing the production process of some end product, is important in various applications, e.g., to assess quality, to ensure reproducibility, or to reinforce trust in the end product. In the past, different types of provenance meta-data have been proposed, each with a different scope. The first part of the proposed tutorial provides an overview and comparison of these different types of provenance. To put provenance to good use, it is essential to be able to interact with and present provenance data in a user-friendly way. Often, users interested in provenance are not necessarily experts in databases or query languages, as they are typically domain experts of the product and production process for which provenance is collected (biologists, journalists, etc.). Furthermore, in some scenarios, it is difficult to use solely queries for analyzing and exploring provenance data. The second part of this tutorial therefore focuses on enabling users to leverage provenance through adapted visualizations. To this end, we will present some fundamental concepts of visualization before we discuss possible visualizations for provenance.