Intermediate representations in Computational Science for knowledge discovery

Abstract

This project focuses on a unified strategy for knowledge and emerging dynamics discovery in Computational Science using intermediate representations. The intended applications are in areas characterized by large volumes of data in which knowledge discovery implies the transition from raw data bases for intermediate representations (usually feature vectors and graphs), thus allowing for the subsequent use of different analytical methods. In this context, integration and transformation methods to be used in the generation of intermediate data should also ensure the quality and reliability of data generated for the intermediate representation. The results of the analysis phase may influence both experiments and the integration methods for generating new data by feedback mechanisms. This project has two general goals: 1) to develop methodologies to solve Computational Science problems based on a common approach of intermediate mathematical-computational representations; 2) to apply the developed methodologies to different scientific problems, thus creating specific solutions to each problem. This methodological strategy will be used to address specific problems in areas which our group has been working in recent years: intermediate representations in computer vision and urban informatics; study of biological networks dynamics to characterize the mechanisms of the health-disease transition; development of computational tools for processing of MRI images high field and their integration with biological data; development of new techniques for characterization and visualization of intermediate representations in complex dynamic networks, with applications in Systems Biology. (AU)