Scalable context-aware role mining with MapReduce

Cloud computing platforms facilitate efficiently processing complicated computing problems of which the time cost used to be unacceptable. Recent research has attempted to use role-based approaches for context-aware service recommendation, yet role mining problem has been proven to be difficult to compute. Currently proposed role-mining algorithms are inefficient and may not scale to cope with the huge amount of data in the real-world. This paper proposes a novel algorithm with much better runtime complexity, and in MapReduce style to take advantage of popular distributed computing platforms. Experiments running on a medium-sized high performance computing cluster demonstrate that our proposed algorithm works well with both running time complexity and scalability.