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|Title:||Optimizing STR algorithms with tuple compression|
|Citation:||Xia, W.,Yap, R.H.C. (2013). Optimizing STR algorithms with tuple compression. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8124 LNCS : 724-732. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-40627-0_53|
|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. © 2013 Springer-Verlag.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
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