Visualization to support identifying relevant attributes in time-varying multidimensional data

• Grant number: 12/24537-0
• Support Opportunities: Scholarships in Brazil - Doctorate
• Effective date (Start): June 01, 2013
• Effective date (End): April 30, 2017
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Maria Cristina Ferreira de Oliveira
• Grantee: Aurea Rossy Soriano Vargas
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated research grant: 11/22749-8 - Challenges in exploratory visualization of multidimensional data: paradigms, scalability and applications, AP.TEM
• Associated scholarship(s): 15/12831-0 - Visual exploration to support the identification of relevant attributes in multidimensional time-varying data, BE.EP.DR

Abstract

The goal of this PhD project is to investigate Multidimensional Visualization techniques to support exploratory data analysis tasks related to identifying relevant attributes to understand the behavior of phenomena involving multiple variables. We seek to investigate and propose a visual framework for analyzing multidimensional data that describe a phenomenon´s behavior over time, assisting data analysts in identifying the relevant attributes. The proposed framework must be interactive and allow users to drive the analysis process and input his/her domain knowledge. This project involves a partnership with researchers from the Department of Cartography of the Faculty of Science and Technology of UNESP at Presidente Prudente. These researchers study the effects of ionospheric scintillation on GPS measurements, and are developing a database that records observations concerning this purpose. This problem features an ideal application domain to identify the requirements and validate the techniques to be proposed inthis project, as it involves the investigation of the role of different attributes in the temporal behavior of the phenomenon. The proposed framework must be applicable to this and other problems of a similar nature.

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

SORIANO-VARGAS, AUREA; HAMANN, BERND; DE OLIVEIRA, MARIA CRISTINA F.. TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data. INFORMATION VISUALIZATION, v. 19, n. 1, JULY 2019. (17/05838-3, 12/24537-0, 15/12831-0)

SORIANO-VARGAS, AUREA; VANI, BRUNO C.; SHIMABUKURO, MILTON H.; MONICO, JOAO F. G.; OLIVEIRA, MARIA CRISTINA F.; HAMANN, BERND. Visual analytics of time-varying multivariate ionospheric scintillation data. COMPUTERS & GRAPHICS-UK, v. 68, p. 96-107, NOV 2017. (11/22749-8, 15/12831-0, 12/24537-0)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

VARGAS, Aurea Rossy Soriano. Visual exploration to support the identification of relevant attributes in time-varying multivariate data. 2018. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.