Thomas Baudel

Visual Analysis of Multi-Dimensional Categorical Data Sets

We present a set of interactive techniques for the visual analysis of multi‐dimensional categorical data. Our approach is based on multiple correspondence analysis (MCA), which allows one to analyse relationships, patterns, trends and outliers among dependent categorical variables. We use MCA as a dimensionality reduction technique to project both observations and their attributes in the same 2D space. We use a treeview to show attributes and their domains, a histogram of their representativity in the data set and as a compact overview of attribute‐related facts. A second view shows both attributes and observations. We use a Voronoi diagram whose cells can be interactively merged to discover salient attributes, cluster values and bin categories. Bar chart legends help assigning meaning to the 2D view axes and 2D point clusters. We illustrate our techniques with real‐world application data.

Capturing the Design Space of Sequential Space-Filling Layouts

We characterize the design space of the algorithms that sequentially tile a rectangular area with smaller, fixed-surface, rectangles. This space consist of five independent dimensions: Order, Size, Score, Recurse and Phrase. Each of these dimensions describe a particular aspect of such layout tasks. This class of layouts is interesting, because, beyond encompassing simple grids, tables and trees, it also includes all kinds of treemaps involving the placement of rectangles. For instance, Slice and dice, Squarified, Strip and Pivot layouts are various points in this five dimensional space. Many classic statistics visualizations, such as 100% stacked bar charts, mosaic plots and dimensional stacking, are also instances of this class. A few new and potentially interesting points in this space are introduced, such as spiral treemaps and variations on the strip layout. The core algorithm is implemented as a JavaScript prototype that can be used as a layout component in a variety of InfoViz toolkits.

Obvious: A meta-toolkit to encapsulate information visualization toolkits — One toolkit to bind them all

This article describes “Obvious”: a meta-toolkit that abstracts and encapsulates information visualization toolkits implemented in the Java language. It intends to unify their use and postpone the choice of which concrete toolkit(s) to use later-on in the development of visual analytics applications. We also report on the lessons we have learned when wrapping popular toolkits with Obvious, namely Prefuse, the InfoVis Toolkit, partly Improvise, JUNG and other data management libraries. We show several examples on the uses of Obvious, how the different toolkits can be combined, for instance sharing their data models. We also show how Weka and Rapid-Miner, two popular machine-learning toolkits, have been wrapped with Obvious and can be used directly with all the other wrapped toolkits. We expect Obvious to start a co-evolution process: Obvious is meant to evolve when more components of Information Visualization systems will become consensual. It is also designed to help information visualization systems adhere to the best practices to provide a higher level of interoperability and leverage the domain of visual analytics.

Decision Exploration Lab: A Visual Analytics Solution for Decision Management

We present a visual analytics solution designed to address prevalent issues in the area of Operational Decision Management (ODM). In ODM, which has its roots in Artificial Intelligence (Expert Systems) and Management Science, it is increasingly important to align business decisions with business goals. In our work, we consider decision models (executable models of the business domain) as ontologies that describe the business domain, and production rules that describe the business logic of decisions to be made over this ontology. Executing a decision model produces an accumulation of decisions made over time for individual cases. We are interested, first, to get insight in the decision logic and the accumulated facts by themselves. Secondly and more importantly, we want to see how the accumulated facts reveal potential divergences between the reality as captured by the decision model, and the reality as captured by the executed decisions. We illustrate the motivation, added value for visual analytics, and our proposed solution and tooling through a business case from the car insurance industry.