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

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

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
283 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)708-735
JournalDATA MINING AND KNOWLEDGE DISCOVERY
Volume32
Early online date15 Feb 2018
DOIs
Publication statusPublished - 30 May 2018

Keywords

  • data provenance
  • data analytics
  • network metrics
  • graph classification

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