Computing minimal hitting sets for a collection of sets is an important problem in many domains (e.g., Spectrum-based Fault Localization). Being an NP-Hard problem, exhaustive algorithms are usually prohibitive for real-world, often large, problems. In practice, the usage of heuristic based approaches trade-off completeness for time efficiency. An example of such heuristic approaches is STACCATO, which was proposed in the context of reasoning-based fault localization. In this paper, we propose an efficient distributed algorithm, dubbed MHS2, that renders the sequential search algorithm STACCATO suitable to distributed, Map-Reduce environments. The results show that MHS2 scales to larger systems (when compared to STACCATO), while entailing either marginal or small run time overhead
