||-ROSETTA

Technology improves every day. In order for an established theory to maintain relevance, implementations of such theory must be updated to take advantage of the new improvements. ROSETTA, a framework based on Rough Set theory, was developed in 1994 to exploit Rough Set paradigms in Machine Learning. Since then, much has happened in the field of Computer Technology, and to fully exploit these benefits ROSETTA needed to evolve. We designed and implemented a multi-core execution process in ROSETTA, optimized for speed and modular extension. The program was tested using four datasets of different sizes for computational speed and memory usage, the factors considered the primary limitations of classification and Machine Learning. The results show an increase in computation speed consistent with expected gains. The scaling per thread of memory usage was less than linear after five threads with increases in memory based primarily on the number of objects in the dataset. The number of features in the data increased the base memory needed but did not significantly impact the memory scaling by threads. The multi-core implementation was successful, and ||-ROSETTA (pronounced Parallel-ROSETTA) is capable of fully exploiting modern hardware solutions.