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Clustering provenance: Facilitating provenance exploration through data abstraction

Lookup NU author(s): Professor Paolo MissierORCiD


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© 2016 Copyright held by the owner/author(s). 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. Our core contribution is the design of a prototype interface that supports clustering and its analytic evaluation in terms of desirable properties of visualisation interfaces.

Publication metadata

Author(s): Karsai L, Fekete A, Kay J, Missier P

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: HILDA 2016 - Proceedings of the Workshop on Human-In-the-Loop Data Analytics

Year of Conference: 2016

Online publication date: 26/06/2016

Acceptance date: 02/04/2016

Publisher: Association for Computing Machinery, Inc


DOI: 10.1145/2939502.2939508

Library holdings: Search Newcastle University Library for this item

ISBN: 9781450342070