Provenance network analytics: an approach to data analytics using data provenance

Huynh, Trung Dong, Ebden, Mark, Fischer, Joel E., Roberts, Stephen and Moreau, Luc (2018) Provenance network analytics: an approach to data analytics using data provenance. Data Mining and Knowledge Discovery . ISSN 1573-756X

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Provenance network analytics is a novel data analytics approach that helps infer properties of data, such as quality or importance, from their provenance. Instead of analysing application data, which are typically domain-dependent, it analyses the data's provenance as represented using the World Wide Web Consortium's domain-agnostic PROV data model. Specifically, the approach proposes a number of network metrics for provenance data and applies established machine learning techniques over such metrics to build predictive models for some key properties of data. Applying this method to the provenance of real-world data from three different applications, we show that it can successfully identify the owners of provenance documents, assess the quality of crowdsourced data, and identify instructions from chat messages in an alternate-reality game with high levels of accuracy. By so doing, we demonstrate the different ways the proposed provenance network metrics can be used in analysing data, providing the foundation for provenance-based data analytics.

Item Type: Article
Keywords: data provenance; data analytics; network metrics; graph classification
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number:
Depositing User: Fischer, Joel
Date Deposited: 04 Jan 2018 09:27
Last Modified: 04 May 2020 19:32

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