Provenance Capture Disparities Highlighted Through Datasets

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.​