Computing Data Cubes Using Massively Parallel Processors

To better support decision making, it was proposed to extend SQL to include data cube operations. Computation of data cube requires computing a number of interrelated group-bys, which is rather expensive operation when databases are large. In this paper, we propose to couple a relational database management system with massively parallel processors (MPP) to facilitate on-line analytic processing. Extended SQL queries involving complex data analysis such as data cube computation are decomposed. Data retrieved from the database are pipelined to the MPP machine where data cubes are computed in parallel. The system architecture and issues related to parallel computation of data cubes are described. A brute force parallel data cube processing algorithm was implemented and the results of some preliminary experiments are presented. 1. Introduction Aggregation is widely used in on-line analytical processing. In relational database systems, SQL supports a set of aggregate functions (SUM, COUN..