Data-intensive applications can benefit of the use of parallel and distributed computing systems both to improve performance and quality of data analysis. Faster processing also means that users can experiment with more models to understand complex data.
High performance makes it practical for users to analyze greater quantities of data.
Both the size of the data available and the complexity of the algorithms required for concept extraction suggest that high-performance parallel and distributed computing has an important role to play. In particular, when very large data sets must be analyzed and manipulated, parallelism is the only possibility if accuracy must be assured. On the other hand, distributed techniques are necessary to handle geographically distributed data repositories. This special issue offers the opportunity to present and discuss problems and report both theoretical and practical solutions in the area, to identify new ideas, and to shape future directions for research. Papers must be written in the Parallel Computing style.