Local clustering in provenance graphs

Authors:
Peter Macko;Daniel Margo;Margo Seltzer
Affiliations:
Harvard University, Cambridge, MA, USA;Harvard University, Cambridge, MA, USA;Harvard University, Cambridge, MA, USA
Venue:
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Year:
2013

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Abstract

Systems that capture and store data provenance, the record of how an object has arrived at its current state, accumulate historical metadata over time, forming a large graph. Local clustering in these graphs, in which we start with a seed vertex and grow a cluster around it, is of paramount importance because it supports critical provenance applications such as identifying semantically meaningful tasks in an object's history. However, generic graph clustering algorithms are not effective at these tasks. We identify three key properties of provenance graphs and exploit them to justify two new centrality metrics we developed for use in performing local clustering on provenance graphs.