Optimizing Semantic Data Transformation using High Performance Computing Techniques
The growth of the Life Science Semantic Web is illustrated by the increasing number of resources available in the Linked Open Data Cloud. Our SWIT tool supports the generation of semantic repositories, and it has been successfully applied in the field of orthology resources, helping to achieve objectives of the Quest for Orthologs consortium. How- ever, our experience with SWIT reveals that the time required for the generation of datasets is longer than desired. In this work we present the application of High Performance Computing techniques, mainly memory optimization and parallelization, to speed up SWIT.