Smart Brushing for Parallel Coordinates

ProvenanceWidgets: A Library of UI Control Elements to Track and Dynamically Overlay Analytic Provenance

We present ProvenanceWidgets, a Javascript library of UI control elements such as radio buttons, checkboxes, and dropdowns to track and dynamically overlay a user's analytic provenance. These in situ overlays not only save screen space but also minimize the amount of time and effort needed to access the same information from elsewhere in the UI. In this paper, we discuss how we design modular UI control elements to track how often and how recently a user interacts with them and design visual overlays showing an aggregated summary as well as a detailed temporal history. We demonstrate the capability of ProvenanceWidgets by recreating three prior widget libraries: (1) Scented Widgets, (2) Phosphor objects, and (3) Dynamic Query Widgets. We also evaluated its expressiveness and conducted case studies with visualization developers to evaluate its effectiveness. We find that ProvenanceWidgets enables developers to implement custom provenance-tracking applications effectively. ProvenanceWidgets is available as open-source software at https://github.com/ProvenanceWidgets to help application developers build custom provenance-based systems.

DimBridge: Interactive Explanation of Visual Patterns in Dimensionality Reductions with Predicate Logic

Dimensionality reduction techniques are widely used for visualizing high-dimensional data. However, support for interpreting patterns of dimension reduction results in the context of the original data space is often insufficient. Consequently, users may struggle to extract insights from the projections. In this paper, we introduce DimBridge, a visual analytics tool that allows users to interact with visual patterns in a projection and retrieve corresponding data patterns. DimBridge supports several interactions, allowing users to perform various analyses, from contrasting multiple clusters to explaining complex latent structures. Leveraging first-order predicate logic, DimBridge identifies subspaces in the original dimensions relevant to a queried pattern and provides an interface for users to visualize and interact with them. We demonstrate how DimBridge can help users overcome the challenges associated with interpreting visual patterns in projections.

Visual Analysis of Multi-Parameter Distributions Across Ensembles of 3D Fields

For an ensemble of 3D multi-parameter fields, we present a visual analytics workflow to analyse whether and which parts of a selected multi-parameter distribution is present in all ensemble members. Supported by a parallel coordinate plot, a multi-parameter brush is applied to all ensemble members to select data points with similar multi-parameter distribution. By a combination of spatial sub-division and a covariance analysis of partitioned sub-sets of data points, a tight partition in multi-parameter space with reduced number of selected data points is obtained. To assess the representativeness of the selected multi-parameter distribution across the ensemble, we propose a novel extension of violin plots that can show multiple parameter distributions simultaneously. We investigate the visual design that effectively conveys (dis-)similarities in multi-parameter distributions, and demonstrate that users can quickly comprehend parameter-specific differences regarding distribution shape and representativeness from a side-by-side view of these plots. In a 3D spatial view, users can analyse and compare the spatial distribution of selected data points in different ensemble members via interval-based isosurface raycasting. In two real-world application cases we show how our approach is used to analyse the multi-parameter distributions across an ensemble of 3D fields.

Augmenting Parallel Coordinates Plots With Color-Coded Stacked Histograms

We introduce Parallel Histogram Plot (PHP), a technique that overcomes the innate limitations of parallel coordinates plot (PCP) by attaching stacked-bar histograms with discrete color schemes to PCP. The color-coded histograms enable users to see an overview of the whole data without cluttering or scalability issues. Each rectangle in the PHP histograms is color coded according to the data ranking by a selected attribute. This color-coding scheme allows users to visually examine relationships between attributes, even between those that are displayed far apart, without repositioning or reordering axes. We adopt the Visual Information Seeking Mantra so that the polylines of the original PCP can be used to show details of a small number of selected items when the cluttering problem subsides. We also design interactions, such as a focus+context technique, to help users investigate small regions of interest in a space-efficient manner. We provide a real-world example in which PHP is effectively utilized compared with other visualizations, and we perform a controlled user study to evaluate the performance of PHP in helping users estimate the correlation between attributes. The results demonstrate that the performance of PHP was consistent in the estimation of correlations between two attributes regardless of the distance between them.

AgentVis: Visual Analysis of Agent Behavior With Hierarchical Glyphs

Glyphs representing complex behavior provide a useful and common means of visualizing multivariate data. However, due to their complex shape, overlapping, and occlusion of glyphs is a common and prominent limitation. This limits the number of discreet data tuples that can be displayed in a given image. Using a real-world application, glyphs are used to depict agent behavior in a call center. However, many call centers feature thousands of agents. A standard approach representing thousands of agents with glyphs does not scale. To accommodate the visualization incorporating thousands of glyphs we develop clustering of overlapping glyphs into a single parent glyph. This hierarchical glyph represents the mean value of all child agent glyphs, removing overlap and reduTcing visual clutter. Multi-variate clustering techniques are explored and developed in collaboration with domain experts in the call center industry. We implement dynamic control of glyph clusters according to zoom level and customized distance metrics, to utilize image space with reduced overplotting and cluttering. We demonstrate our technique with examples and a usage scenario using real-world call-center data to visualize thousands of call center agents, revealing insight into their behavior and reporting feedback from expert call-center analysts.

Feature-Rich, GPU-Assisted Scatterplots for Millions of Call Events

The contact center industry represents a large proportion of many country’s economies. For example, 4% of the entire United States and UK’s working population is employed in this sector. As in most modern industries, contact centers generate gigabytes of operational data that require analysis to provide insight and to improve efficiency. Visualization is a valuable approach to data analysis, enabling trends and correlations to be discovered, particularly when using scatterplots. We present a feature-rich application that visualizes large call center data sets using scatterplots that support millions of points. The application features a scatterplot matrix to provide an overview of the call center data attributes, animation of call start and end times, and utilizes both the CPU and GPU acceleration for processing and filtering. We illustrate the use of the Open Computing Language (OpenCL) to utilize a commodity graphics card for the fast filtering of fields with multiple attributes. We demonstrate the use of the application with millions of call events from a month’s worth of real-world data and report domain expert feedback from our industry partner.