Clustering provenance: Facilitating provenance exploration through data abstraction

Linus Karsai; Alan Fekete; Judy Kay; Paolo Missier

doi:10.1145/2939502.2939508

Clustering provenance: Facilitating provenance exploration through data abstraction

Linus Karsai, Alan Fekete, Judy Kay, Paolo Missier

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English
Title of host publication	HILDA 2016 - Proceedings of the Workshop on Human-In-the-Loop Data Analytics
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450342070
DOIs	https://doi.org/10.1145/2939502.2939508
Publication status	Published - 26 Jun 2016
Event	1st Workshop on Human-in-the-Loop Data Analytics, HILDA 2016 - San Francisco, United States Duration: 26 Jun 2016 → …

Publication series

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

Conference

Conference	1st Workshop on Human-in-the-Loop Data Analytics, HILDA 2016
Country/Territory	United States
City	San Francisco
Period	26/06/16 → …

Bibliographical note

Publisher Copyright:
© 2016 Copyright held by the owner/author(s).

Keywords

Large-scale graphs
Provenance
Visualisation

ASJC Scopus subject areas

Computer Science Applications
Information Systems
Computational Theory and Mathematics

Access to Document

10.1145/2939502.2939508

Cite this

Karsai, L., Fekete, A., Kay, J., & Missier, P. (2016). Clustering provenance: Facilitating provenance exploration through data abstraction. In HILDA 2016 - Proceedings of the Workshop on Human-In-the-Loop Data Analytics (HILDA 2016 - Proceedings of the Workshop on Human-In-the-Loop Data Analytics). Association for Computing Machinery . https://doi.org/10.1145/2939502.2939508

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abstract = "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.",

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Karsai, L, Fekete, A, Kay, J & Missier, P 2016, Clustering provenance: Facilitating provenance exploration through data abstraction. in HILDA 2016 - Proceedings of the Workshop on Human-In-the-Loop Data Analytics. HILDA 2016 - Proceedings of the Workshop on Human-In-the-Loop Data Analytics, Association for Computing Machinery , 1st Workshop on Human-in-the-Loop Data Analytics, HILDA 2016, San Francisco, United States, 26/06/16. https://doi.org/10.1145/2939502.2939508

Clustering provenance: Facilitating provenance exploration through data abstraction. / Karsai, Linus; Fekete, Alan; Kay, Judy et al.
HILDA 2016 - Proceedings of the Workshop on Human-In-the-Loop Data Analytics. Association for Computing Machinery , 2016. (HILDA 2016 - Proceedings of the Workshop on Human-In-the-Loop Data Analytics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Clustering provenance: Facilitating provenance exploration through data abstraction

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

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