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