Optimizing STR Algorithms with Tuple Compression

Abstract

Table constraints define an arbitrary constraint explicitly as a set of solutions (tuples) or non-solutions. Thus, space is proportional to number of tuples. Simple Tabular Reduction (STR), which dynamically reduces the table size by maintaining a table of only the valid tuples, has been shown to be efficient for enforcing Generalized Arc Consistency. The Cartesian product representation is another way of having a smaller table by compression. We investigate whether STR and the Cartesian product representation can work hand in hand. Our experiments show the compression-based STR can be faster once the tables compress well. Thus, the benefits of the STR2 and STR3 algorithms respectively are retained while consuming less space.

This work has been supported by grant MOE2012-T2-1-155.