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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Roper, B., Chapman, A., Martin, D., Morley, J. (2018). A Graph Testing Framework for Provenance Network Analytics. In: Belhajjame, K., Gehani, A., Alper, P. (eds) Provenance and Annotation of Data and Processes. IPAW 2018. Lecture Notes in Computer Science(), vol 11017. Springer, Cham. https://doi.org/10.1007/978-3-319-98379-0_29
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DOI: https://doi.org/10.1007/978-3-319-98379-0_29
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