Exploiting Functional Decomposition for Efficient Parallel Processing of Multiple Data Analysis Queries

Reuse is a powerful method for improving system performance. In this paper, we examine functional decomposition for improving data reuse and, therefore, overall query execution performance in the context of data analysis applications. Additionally, we look at the performance effects of using various projection primitives that make it possible to transform intermediate results generated during the execution of a previous query so that they can be reused by a new query. A satellite data analysis application is used to experimentally show the performance benefits achieved using these strategies.