David Auber

Winding roads: routing edges into bundles

Visualizing graphs containing many nodes and edges efficiently is quite challenging. Drawings of such graphs generally suffer from visual clutter induced by the large amount of edges and their crossings. Consequently it is difficult to read the relationships between nodes and the high‐level edge patterns that may exist in standard node‐link diagram representations. Edge bundling techniques have been proposed to help solve this issue, which rely on high quality edge rerouting. In this paper, we introduce an intuitive edge bundling technique which efficiently reduces edge clutter in graphs drawings. Our method is based on the use of a grid built using the original graph to compute the edge rerouting. In comparison with previously proposed edge bundling methods, our technique improves both the level of clutter reduction and the computation performance. The second contribution of this paper is a GPU‐based rendering method which helps users perceive bundles densities while preserving edge color.

3D Edge Bundling for Geographical Data Visualization

Visualization of graphs containing many nodes and edges efficiently is quite challenging since representations generally suffer from visual clutter induced by the large amount of edge crossings and node-edge overlaps. That problem becomes even more important when nodes positions are fixed, such as in geography were nodes positions are set according to geographical coordinates. Edge bundling techniques can help to solve this issue by visually merging edges along common routes but it can also help to reveal high-level edge patterns in the network and therefore to understand its overall organization. In this paper, we present a generalization of [18] to reduce the clutter in a 3D representation by routing edges into bundles as well as a GPU-based rendering method to emphasize bundles densities while preserving edge color. To visualize geographical networks in the context of the globe, we also provide a new technique allowing to bundle edges around and not across it.

From Databases to Graph Visualization

The first step of any information visualization system is to enable end user to import their dataset into the system. However, non expert user are faced to the difficult task of choosing how their data should/could be transform to be used in these Infovis systems. In that paper we address the case where end users want to use dataset in tabular format. We propose a novel method for automatic graph generation from these datasets. That method consists in first building taxonomy of dimensions. Then, that taxonomy is used to provide to user a system that enables to interactively navigate into the set of possible data transformation.

HeatPipe: High Throughput, Low Latency Big Data Heatmap with Spark Streaming

Heatmap visualization is a well-known type of visualization to alleviate the overplot problem of point visualization. As such, it is well suited to visualize Big Data. In order to tackle the velocity problem of Big Data, one has to leverage streaming computations. Recently, canopy clustering was shown to be well suited for Big Data heatmap visualization. In this article, we present how to design a streaming algorithm to compute canopy clustering using Apache Spark. This result is directly applicable to be included into a lambda architecture.