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Data analytics often involves hypothetical reasoning: repeatedly modifyingthe data and observing the induced effect on the computation result of adata-centric application. Recent work has proposed to leverage ideas from dataprovenance tracking towards supporting efficient hypothetical reasoning:instead of a costly re-execution of the underlying application, one may assignvalues to a pre-computed provenance expression. A prime challenge in leveragingthis approach for large-scale data and complex applications lies in the size ofthe provenance. To this end, we present a framework that allows to reduceprovenance size. Our approach is based on reducing the provenance granularityusing abstraction. We propose a demonstration of COBRA, a system that allowsexamine the effect of the provenance compression on the anticipated analysisresults. We will demonstrate the usefulness of COBRA in the context of businessdata analysis.

COBRA: Compression via Abstraction of Provenance for Hypothetical Reasoning