Scaling SPADE to Big provenance

Provenance middleware (such as SPADE) lets individuals and applications use a common framework for reporting, storing, and querying records that characterize the history of computational processes and resulting data artifacts. Previous efforts have addressed a range of issues, from instrumentation techniques to applications in the domains of scientific reproducibility and data security. Here we report on our experience adapting SPADE to handle large provenance data sets. In particular, we describe two motivating case studies, several challenges that arose from managing provenance at scale, and our approach to address each concern.