| Grant number: | 17/05838-3 |
| Support Opportunities: | Regular Research Grants |
| Start date: | July 01, 2017 |
| End date: | December 31, 2019 |
| Field of knowledge: | Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques |
| Principal Investigator: | Maria Cristina Ferreira de Oliveira |
| Grantee: | Maria Cristina Ferreira de Oliveira |
| Host Institution: | Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil |
| City of the host institution: | São Carlos |
| Associated researchers: | Alneu de Andrade Lopes ; Rosane Minghim |
Abstract
Research on Visual Analytics research is central to addressing the challenges in data analysis and data-intensive computing, in view of its potential of combining Machine Learning and Visualization techniques to assist human interpretation of complex datasets. Coupling techniques from both areas can promote significant advances in data analysis capabilities, as human and computer can take on complementary roles in addressing the many challenges introduced by the volume and complexity of datasets generated in wide variety of application domains. This project addresses two distinct research problems in visual analytics, focusing both in an applied problem and in a conceptual problem. In its applied focus, the project shall consider (i) the visualization of large scale networks, with a particular interest in solutions applicable to visualizing social networks; and (ii) visualization to support exploratory analysis of attribute spaces that characterize multivariate and time-varying phenomena. One example is datasets produced by sensors employed for environmental monitoring. In both cases, the search for scalable solutions capable of handling large volumes of data poses a major research challenge. As for the conceptual question, in an ongoing collaboration we have been investigating approaches to clarify the cognitive processes underlying human interpretation of a particular type of multidimensional visualization, the so-called similarity maps. In this project we will design and carry out some experimental studies that might contribute to such an understanding. Results from these studies may shed light on possible conceptual models of the interpretation of this particular category of visual mapping, and hopefully contribute to establishing a foundation for the usage of these techniques, which is essential for further advances in the field. (AU)
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