Data Compression Techniques and Algorithms for Effectively and Efficiently Managing Multidimensional Stream Cubes over Grids

The problem of managing multidimensional stream cubes (i.e., data cubes originated from data streams) over Computational Grids still plays a critical role in Database and Data Warehousing research, since it covers a wide family of real-life application scenarios. Despite recent technological advancements, high dimensionality and massive size are still the most significant challenges to be addressed. In this respect, the usage of data compression techniques and algorithms is a well-suited and well-understood solution to deal with managing stream cubes over Grids. Inspired by these motivations, in this paper we provide two state-of-the-art techniques, and discuss open issues and future research directions in this scientific area.