A graph testing framework for provenance network analytics

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.