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Visual analytics: applications and a conceptual investigation

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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VEICULO: TITULO (DATA)
VEICULO: TITULO (DATA)

Scientific publications (7)
(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)
VALEJO, ALAN; FALEIROS, THIAGO; FERREIRA DE OLIVEIRA, MARIA CRISTINA; LOPES, ALNEU DE ANDRADE. A coarsening method for bipartite networks via weight-constrained label propagation. KNOWLEDGE-BASED SYSTEMS, v. 195, MAY 11 2020. Web of Science Citations: 0.
VALEJO, ALAN; FERREIRA, VINICIUS; FABBRI, RENATO; FERREIRA DE OLIVEIRA, MARIA CRISTINA; LOPES, ALNEU DE ANDRADE. A Critical Survey of the Multilevel Method in Complex Networks. ACM COMPUTING SURVEYS, v. 53, n. 2 APR 2020. Web of Science Citations: 0.
VALEJO, ALAN; GOES, FABIANA; ROMANETTO, LUZIA; FERREIRA DE OLIVEIRA, MARIA CRISTINA; LOPES, ALNEU DE ANDRADE. A benchmarking tool for the generation of bipartite network models with overlapping communities. KNOWLEDGE AND INFORMATION SYSTEMS, OCT 2019. Web of Science Citations: 0.
REIS, CLAUSIUS D. G.; PADOVESE, LINILSON RODRIGUES; DE OLIVEIRA, MARIA C. F. Automatic detection of vessel signatures in audio recordings with spectral amplitude variation signature. METHODS IN ECOLOGY AND EVOLUTION, v. 10, n. 9, p. 1501-1516, SEP 2019. Web of Science Citations: 0.
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. Web of Science Citations: 0.
PAULOVICH, V, FERNANDO; DE OLIVEIRA, MARIA CRISTINA F.; OLIVEIRA JR, OSVALDO N. A Future with Ubiquitous Sensing and Intelligent Systems. ACS SENSORS, v. 3, n. 8, p. 1433-1438, AUG 2018. Web of Science Citations: 8.
VALEJO, ALAN; FERREIRA DE OLIVEIRA, MARIA CRISTINA; FILHO, GERALDO P. R.; LOPES, ALNEU DE ANDRADE. Multilevel approach for combinatorial optimization in bipartite network. KNOWLEDGE-BASED SYSTEMS, v. 151, p. 45-61, JUL 1 2018. Web of Science Citations: 2.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.