Provenance Network Analytics: An approach to data analytics using data provenance

Huynh, Trung Dong; Ebden, Mark; Fischer, Joel; Roberts, Stephen; Moreau, Luc

Abstract

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.

Citation

Huynh, T. D., Ebden, M., Fischer, J., Roberts, S., & Moreau, L. (2018). Provenance Network Analytics: An approach to data analytics using data provenance. Data Mining and Knowledge Discovery, 32(3), 708-735. https://doi.org/10.1007/s10618-017-0549-3

• Journal Article Type: Article
• Acceptance Date: Dec 26, 2017
• Online Publication Date: Feb 15, 2018
• Publication Date: 2018-05
• Deposit Date: Jan 4, 2018
• Publicly Available Date: Feb 15, 2018
• Journal: Data Mining and Knowledge Discovery
• Print ISSN: 1384-5810
• Electronic ISSN: 1573-756X
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 32
• Issue: 3
• Pages: 708-735
• DOI: https://doi.org/10.1007/s10618-017-0549-3
• Keywords: data provenance; data analytics; network metrics; graph classification
• Public URL: https://nottingham-repository.worktribe.com/output/911918
• Publisher URL: https://link.springer.com/article/10.1007/s10618-017-0549-3