ABSTRACT
Visualization is a highly dynamic field. As the complexity, diversity, and size of datasets generated by current applications increase, new methods and tools emerge every year at a high pace in order to provide more effective and efficient ways to understand such datasets. Scientific visualization is arguably the oldest and most mature branch of data visualization. Information visualization shows a somewhat different picture. Scientific and information visualization are not two clearly separate branches of the same field that evolve in parallel. Similar reflections appeared in the context of the actual effectiveness of visualization used in the context of software engineering. Finding out where existing tools and techniques can apply directly, where they need adaptation, and where fundamentally new “custom point” solutions are required, is probably one of the greatest challenges in designing successful visualizations. There exist also a number of old problems, such as the visualization of time-dependent, three-dimensional flow fields, that are not yet considered to be fully solved.
