Visual Analytics - Introduction (1)

In this video lecture we explain the term „visual analytics“, the motivation for this still rather new scientific fields. This includes a discussion of example problems where visual analytics is beneficial as well as limitations, e.g., problems, where visual analytics is not recommended. This introduction also explains which computer science fields contribute to visual analytics and which components visual analytics systems typically have. It turns out that visual analytics is usually applied to large, complex, often only partially reliable or incomplete data that may be temporal or may include a spatial distribution. Scaleability, in particular with respect to the computational effort involved, is an essential aspect. While analytical and automatic components are key parts of visual analytics, it is a human analyst or often a team of analysts that steers the process. Chapters: 00:00 - What is visual analytics? 21:44 - Why visual analytics? 35:50 - Introduction 41:22 - Brief History 46:22 - Involved Disciplines 52:17 - Scaleability 53:41 - Components of Visual Analytics