Aggregation by Provenance Types: A Technique for Summarising Provenance Graphs

Abstract: As users become confronted with a deluge of provenance data, dedicated techniques are required to make sense of this kind of information. We present Aggregation by Provenance Types, a provenance graph analysis that is capable of generating provenance graph summaries. It proceeds by converting provenance paths up to some length k to attributes, referred to as provenance types, and by grouping nodes that have the same provenance types. The summary also includes numeric values representing the frequency of nodes and edges in the original graph.Quantitative and qualitative evaluations and a complexity analysis show that this technique is tractable; with small values of k, it can produce useful summaries and can help detect outliers. We illustrate how the generated summaries can further be used for conformance checking and visualization.


Citation: Luc Moreau. Aggregation by provenance types: A technique for summarising provenance graphs. In Graphs as Models 2015 (An ETAPS’15 workshop), London, UK, April 2015.


About P. Andreadis

Pre-Doctoral Research Assistant in AI and Social Computation @ University of Edinburgh.

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