Provenance Capture Disparities Highlighted Through Datasets

May 2014
Topics: Computing Methodologies, Data (General), Special-Purpose and Application-Based Systems
G. Blake Coe, The MITRE Corporation
R. Christopher Doty, Georgia Institute of Technology
M. David Allen, The MITRE Corporation
Dr. Adriane P. Chapman, The MITRE Corporation
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Provenance information is inherently affected by the method of its capture. Different capture mechanisms create very different provenance graphs. In this work, we describe an academic use case that has corollaries in offices everywhere. We also describe two distinct possibilities for provenance capture methods within this domain. We generate three datasets using these two capture methods: the capture methods run individually and a trace of what an omniscient capture agent would see. We describe how the different capture methods lead to such very different graphs and release the graphs for others to use via the ProvBench effort.​


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