Animated Visualization of Causal Relations Through Growing 2D Geometry

Abstract

Causality visualization is an important tool for many scientific domains that involve complex interactions between multiple entities (examples include parallel and distributed systems in computer science). However, traditional visualization techniques such as Hasse diagrams are not well-suited to large system executions, and users often have difficulties answering even basic questions using them, or have to spend inordinate amounts of time to do so. In this paper, we present the Growing Squares and Growing Polygons methods, two sibling visualization techniques that were designed to solve this problem by providing efficient 2D causality visualization through the use of color, texture, and animation. Both techniques have abandoned the traditional linear timeline and instead map the time parameter to the size of geometrical primitives representing the processes; in the Growing Squares case, each process is a color-coded square that receives color influences from other process squares as messages reach it; in the Growing Polygons case, each process is instead an n-sided polygon consisting of triangular sectors showing color-coded influences from the other processes. We have performed user studies of both techniques, comparing them with Hasse diagrams, and they have been shown to be significantly more efficient than old techniques, both in terms of objective performance as well as the subjective opinion of the test subjects (the Growing Squares technique is, however, only significantly more efficient for small systems).