Visualization of Causal Relations

The notion of cause and effect is pervasive in human thinking and plays a significant role in our perception of time. Software systems, in particular parallel and distributed ones, are permeated by this causality, and the human mind is especially well-suited to detect instances of this concept. Unfortunately, real-world systems of causally related events are often too large and complex to be comprehended unaided. In this thesis, we explore ways of using information visualization to help humans perceive these complex systems of causal relations, not only for software systems, but also for more general application areas.The Growing Squares visualization technique uses a combination of color, texture, and animation to present a sequence of related events in a distributed system. User studies show that this technique is significantly more effective for solving problems related to causality in distributed systems than traditional Hasse diagrams for small data sets, and more effective (though not significantly so) for large data sets.The Growing Polygons visualization technique was designed to address some of the weaknesses of the Growing Squares technique, and presents the interacting processes in a system as color-coded polygons with sectors indicating the influences and information propagation in the system. User studies show that this technique is significantly more effective than Hasse diagrams for all data sets, regardless of size.Finally, we have conducted a case study of causality visualization in the context of scientific citation networks, creating a bibliographic visualization tool called CiteWiz. The tool contains a modified Growing Polygons visualization, suitably adapted to citation networks with linear time windows and process hierarchies, as well as a new static timeline visualization that maps the citation count and publication date of an article or author to its size and position on the timeline, respectively