Reordering - Optimizing techniques for visual data structure reordering

• Grant number: 15/14854-7
• Support Opportunities: Regular Research Grants
• Start date: March 01, 2016
• End date: February 28, 2018
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Celmar Guimarães da Silva
• Grantee: Celmar Guimarães da Silva
• Host Institution: Faculdade de Tecnologia (FT). Universidade Estadual de Campinas (UNICAMP). Limeira , SP, Brazil

Abstract

Using interactive charts for representing datasets may support data understanding and decision making. In order to improve user comprehension of these datasets, axes-based charts (such as heatmaps, parallel coordinate charts and charts based on pixel-based techniques) enable elements to be reordered inside an axis. This feature helps to highlight patterns and tendencies that may be hidden in the datasets. We highlight the use of PQR tree based matrix reordering methods, among others, which may be applied to heatmaps. However, this class of methods was not applied yet to multidimensional datasets. It was little studied regarding proximity (1-mode) matrix reordering, and it also lacks on scalability of the number of tuples. Besides, it was not applied to other axes-based charts. Therefore, this project is part of a bigger research which aims to refine visual structure reordering algorithms, based on PQR trees. Our goal is to make those algorithms useful for reordering distinct types of multidimensional visual representations, aiming to overcome the performance of other reordering algorithms regarding optimization of runtime and evaluation functions. This project, in particular, focus on matrix reordering, given that matrices are the basis for heatmaps. We aim to reach good reordering results through refinements of SMB (Smoothed Multiple Binarization) algorithm, which uses PQR trees, smoothing and feature vector analysis of datasets. (AU)

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)

DE OLIVEIRA, MAURICIO ROSSI; DA SILVA, CELMAR GUIMARAES; LINSEN, L; TELEA, A; BRAZ, J. Adapting Heuristic Evaluation to Information Visualization A Method for Defining a Heuristic Set by Heuristic Grouping. PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISIGRAPP 2017), VOL 3, v. N/A, p. 8-pg., 2017-01-01. (15/14854-7)

DA SILVA, CELMAR GUIMARAES; MEDINA, BRUNO FIGUEIREDO; DA SILVA, MARESSA RODRIGUES; KAWAKAMI, WILLIAN HITOSHI; NAVES ROCHA, MIGUEL MECHI. A fast feature vector approach for revealing simplex and equi-correlation data patterns in reorderable matrices. INFORMATION VISUALIZATION, v. 16, n. 4, p. 261-274, OCT 2017. (15/14854-7, 14/11186-0, 15/00411-6)