Abstract: Data cube and its pre-computation have been playing an essential role in fast OLAP (online analytical processing) in many data warehouses. For the cube with d dimensions, it can generate 2\+d cuboids and ∏di=1(|D\-i|+1) aggregate cells. But in a high-dimensional cube, it might not be practical to build all these cuboids. In this paper, a novel parallel and distributed storage structure is proposed for high-dimensional cube based on shell segment mini-cubes (DHMC). DHMC partitions the high dimensional cube into some low-dimensional shell segment mini-cubes. OLAP queries are computed online by dynamically constructing cuboids from these shell segment mini-cubes through the parallel & distributed processing system. With this design, for high-dimensional OLAP, the total space that needs to store such shell segment mini-cubes is negligible in comparison with a high-dimensional cube. Such an approach permits a significant reduction of CPU and I/O overhead for many queries by restricting the number of cube segments to be processed for both the fact table and bitmap indices. The proposed data allocation and processing model supports parallel I/O and parallel processing, as well as load balancing for disks and processors. The methods of shell mini-cube are compared with other existing ones such as full cube and partial cube. The analytical and experimental results show that the algorithms of DHMC proposed are more efficient than the other existing ones.