@article {17885,
	title = {Efficient Execution of Multi-Query Data Analysis Batches Using Compiler Optimization Strategies},
	journal = {Languages and Compilers for Parallel Computing},
	year = {2004},
	month = {2004///},
	abstract = {This work investigates the leverage that can be obtained from compiler optimization techniques for efficient execution of multi-query workloads in data analysis applications. Our approach is to address multi-query optimization at the algorithmic level, by transforming a declarative specification of scientific data analysis queries into a high-level imperative program that can be made more efficient by applying compiler optimization techniques. These techniques {\textendash} including loop fusion, common subexpression elimination and dead code elimination {\textendash} are employed to allow data and computation reuse across queries. We describe a preliminary experimental analysis on a real remote sensing application that analyzes very large quantities of satellite data. The results show our techniques achieve sizable reductions in the amount of computation and I/O necessary for executing query batches and in average execution times for the individual queries in a given batch.},
	doi = {10.1007/978-3-540-24644-2_33},
	author = {Andrade,H. and Aryangat,S. and Kurc, T. and Saltz, J. and Sussman, Alan}
}
