Browse by author
Lookup NU author(s): Professor Paolo MissierORCiD
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
© 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.
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
Library holdings: Search Newcastle University Library for this item