Query processing for these applications require different views of data for decision support. The Data Cube operator provides multi-dimensional aggregates, used to calculate and store summary information on a number of dimensions.
Relational databases are scalable in size and efforts are on to make their performance acceptable.
On the other hand multi-dimensional databases perform well for such queries, although they are not very scalable. Parallel computing is necessary to address the scalability and performance issues for these data sets. In this paper we present a parallel and scalable infrastructure for OLAP and multidimensional analysis. Chunks provide a multidimensional index structure for efficient dimension oriented data accesses much the same as md-arrays do. Operations within chunks and between chunks are a combination of relational and multi-dimensional operations depending on whether the chunk is sparse or dense.