A graph testing framework for provenance network analytics
A graph testing framework for provenance network analytics
Provenance Network Analytics is a method of analyzing provenance that assesses a collection of provenance graphs by training a machine learning algorithm to make predictions about the characteristics of data artifacts based on their provenance graph metrics. The shape of a provenance graph can vary according the modelling approach chosen by data analysts, and this is likely to affect the accuracy of machine learning algorithms, so we propose a framework for capturing provenance using semantic web technologies to allow use of multiple provenance models at runtime in order to test their effects.
Analytics, Graph, Network
245-251
Roper, Bernard
1d217e9e-9d47-44c2-bfc0-47e845b74b81
Chapman, Adriane
721b7321-8904-4be2-9b01-876c430743f1
Martin, David
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Morley, Jeremy
21b99007-07ee-4ece-843a-22d720b149b3
Roper, Bernard
1d217e9e-9d47-44c2-bfc0-47e845b74b81
Chapman, Adriane
721b7321-8904-4be2-9b01-876c430743f1
Martin, David
e5c52473-e9f0-4f09-b64c-fa32194b162f
Morley, Jeremy
21b99007-07ee-4ece-843a-22d720b149b3
Roper, Bernard, Chapman, Adriane, Martin, David and Morley, Jeremy
(2018)
A graph testing framework for provenance network analytics.
In Provenance and Annotation of Data and Processes - 7th International Provenance and Annotation Workshop, IPAW 2018, Proceedings.
vol. 11017 LNCS,
Springer Verlag.
.
(doi:10.1007/978-3-319-98379-0_29).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Provenance Network Analytics is a method of analyzing provenance that assesses a collection of provenance graphs by training a machine learning algorithm to make predictions about the characteristics of data artifacts based on their provenance graph metrics. The shape of a provenance graph can vary according the modelling approach chosen by data analysts, and this is likely to affect the accuracy of machine learning algorithms, so we propose a framework for capturing provenance using semantic web technologies to allow use of multiple provenance models at runtime in order to test their effects.
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More information
e-pub ahead of print date: 6 September 2018
Venue - Dates:
7th International Provenance and Annotation Workshop, IPAW 2018, United Kingdom, 2018-07-08 - 2018-07-09
Keywords:
Analytics, Graph, Network
Identifiers
Local EPrints ID: 425162
URI: http://eprints.soton.ac.uk/id/eprint/425162
ISSN: 0302-9743
PURE UUID: c914e78d-681d-4fa5-932f-35acc053c689
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Date deposited: 11 Oct 2018 16:30
Last modified: 07 Oct 2020 02:15
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Contributors
Author:
Bernard Roper
Author:
Jeremy Morley
University divisions
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