Data analytics in general, and data mining primitives in particular , are a major source of bottlenecks in the operation of information systems. This is mainly due to their high complexity and intensive call to IO operations, particularly in massively distributed environments. Moreover , an important application of data analytics is to discover key insights from the running traces of information system in order to improve their engineering. Mining closed frequent itemsets (CFI) is one of these data mining techniques, associated with great challenges. It allows discovering itemsets with better efficiency and result compactness. However, discovering such itemsets in massively distributed data poses a number of issues that are not addressed by traditional methods. One solution for dealing with such characteristics is to take advantage of parallel frameworks like, e.g., MapReduce. We address the problem of distributed CFI mining by introducing a new parallel algorithm, called DCIM, which uses a prime number based approach. A key feature of DCIM is the deep combination of data mining properties with the principles of massive data distribution. We carried out exhaustive experiments over real world datasets to illustrate the efficiency of DCIM for large real world datasets with up to 53 million documents.