  Data analytics often involves hypothetical reasoning: repeatedly modifying
the data and observing the induced effect on the computation result of a
data-centric application. Previous work has shown that fine-grained data
provenance can help make such an analysis more efficient: instead of a costly
re-execution of the underlying application, hypothetical scenarios are applied
to a pre-computed provenance expression. However, storing provenance for
complex queries and large-scale data leads to a significant overhead, which is
often a barrier to the incorporation of provenance-based solutions.
  To this end, we present a framework that allows to reduce provenance size.
Our approach is based on reducing the provenance granularity using user defined
abstraction trees over the provenance variables; the granularity is based on
the anticipated hypothetical scenarios. We formalize the tradeoff between
provenance size and supported granularity of the hypothetical reasoning, and
study the complexity of the resulting optimization problem, provide efficient
algorithms for tractable cases and heuristics for others. We experimentally
study the performance of our solution for various queries and abstraction
trees. Our study shows that the algorithms generally lead to substantial
speedup of hypothetical reasoning, with a reasonable loss of accuracy.
