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A graph testing framework for provenance network analytics

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
0302-9743
245-251
Springer Verlag
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
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. pp. 245-251 . (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, London, United Kingdom, 2018-07-09 - 2018-07-10
Keywords: Analytics, Graph, Network

Identifiers

Local EPrints ID: 425162
URI: https://eprints.soton.ac.uk/id/eprint/425162
ISSN: 0302-9743
PURE UUID: c914e78d-681d-4fa5-932f-35acc053c689
ORCID for Adriane Chapman: ORCID iD orcid.org/0000-0002-3814-2587
ORCID for David Martin: ORCID iD orcid.org/0000-0003-0397-0769

Catalogue record

Date deposited: 11 Oct 2018 16:30
Last modified: 20 Jul 2019 01:21

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Contributors

Author: Bernard Roper
Author: Adriane Chapman ORCID iD
Author: David Martin ORCID iD
Author: Jeremy Morley

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