Data warehouses provide the primary support for Decision Support Systems (DSS) and Business Intelligence (BI) systems. One of the most interesting recent themes in this respect has been the computation and manipulation of the data cube, a relational model that can be used to support On-Line Analytical Processing (OLAP). Within the context of massive data volumes, data cube compression is not only crucial for computing and storing data cubes in limited space, but also reduces I/O access time.; This thesis proposes an efficient data cube compression algorithm and integrates this algorithm into the PANDA system, a mature and well-studied framework for parallel OLAP computation. The experimental results demonstrate that, within the PANDA environment, the algorithm significantly reduces data cube storage space without sacrificing running time. Experimental results also demonstrate that the proposed algorithm and its supporting data structures are well suited for use with the Hilbert Space Filling Curve, a mechanism used in the PANDA system to support the generation and manipulation of multi-dimensional data cubes.
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