Clustering display for provenance data


As digital objects become increasingly important in people’s lives, people may need to understand the provenance, or lineage and history, of an important digital object, to understand how it was produced. This is particularly important for objects created from large, multi-source collections of personal data. As the metadata describing provenance, Provenance Data, is commonly represented as a labelled directed acyclic graph, the challenge is to create effective interfaces onto such graphs so that people can understand the provenance of key digital objects. This unsolved problem is especially challenging for the case of novice and intermittent users and complex provenance graphs. We tackle this by creating an interface based on a clustering approach. This was designed to enable users to view provenance graphs, and to simplify complex graphs by combining several nodes.

Linus Karsai

Image credit: Helping by Anthony Albright via Creative Commons Attribution-ShareAlike 2.0 Generic licence.