Abstract: Scatterplots provide a visual representation of bivariate data (or 2D embeddings of multivariate data) that allows for effective analyses of data dependencies, clusters, trends, and outliers. Unfortunately, classical scatterplots suffer from scalability issues, since growing data sizes eventually lead to overplotting and visual clutter on a screen with fixed resolution, which hinder the data analysis process. We propose an algorithm that compensates irregular sample distributions by a smooth transformation of the scatterplot's visual domain. Our algorithm evaluates the scatterplot's density distribution to compute a regularization mapping based on integral images of the rasterized density function. The mapping preserves the samples' neighborhood relations. Few regularization iterations suffice to achieve a nearly uniform sample distribution that efficiently uses the available screen space. We further propose approaches to visually convey the transformation that was applied to the scatterplot and compare them in a user study. We present a novel parallel algorithm for fast GPU-based integral-image computation, which allows for integrating our de-cluttering approach into interactive visual data analysis systems.

Vladimir Molchanov cursou o bacharelado e mestrado em Matemática Aplicada da Universidade Estadual de Novosibirsk, Rússia. Em seguida, recebeu o título de PhD pela Jacobs University, Bremen, Alemanha, em 2008. Atualmente é pesquisador associado da Universidade de Münster, Alemanha. Seus interesses de pesquisa incluem visualização de dados multidimensionais usando métodos de projeção, sistemas interativos para análise de dados e análise de dados biomédicos.