Massive simulations and arrays of sensing devices, in combination with increasing
computing resources, have generated large, complex, high-dimensional datasets used to …

Visualization is the process of representing data, information, and knowledge in a visual
form to support the tasks of exploration, confirmation, presentation, and understanding. This …

Scatterplots remain one of the most popular and widely-used visual representations for
multidimensional data due to their simplicity, familiarity and visual clarity, even if they lack …

Data quality issues such as missing, erroneous, extreme and duplicate values undermine
analysis and are time-consuming to find and fix. Automated methods can help identify …

Categorical data dimensions appear in many real-world data sets, but few visualization
methods exist that properly deal with them. Parallel Sets are a new method for the …

In the context of data visualization, a glyph is a visual representation of a piece of data
where the attributes of a graphical entity are dictated by one or more attributes of a data …

Visualizing outliers in massive datasets requires statistical pre-processing in order to reduce
the scale of the problem to a size amenable to rendering systems like D3, Plotly or analytic …

The discrete nature of categorical data makes it a particular challenge for visualization.
Methods that work very well for continuous data are often hardly usable with categorical …

In the age of big data, being able to make sense of data is an important key to success.
Interactive Visual Data Analysis advocates the synthesis of visualization, interaction, and …

Correlation analysis can reveal the complex relationships that often exist among the
variables in multivariate data. However, as the number of variables grows, it can be difficult …