Visual Exploration

The aim pursued with visual exploration is to give an overview of the data and to allow users to interactively browse through different portions of the data. In this scenario users have no or only vague hypotheses about the data; their aim is to find some. In this sense, visual exploration can be understood as an undirected search for relevant information within the data. To support users in the search process, a high degree of interactivity must be a key feature of visual exploration techniques.

[Tominski, 2006]

Exploration denotes an undirected search for interesting features in a data set.

[Kreuseler et al., 2004]

Exploratory data analysis as the process of searching and analyzing databases to find implicit but potentially useful information, is a difficult task. At the beginning, the analyst has no hypothesis about the data. According to John Tuckey, tools as well as understanding are needed [Tukey, 1977] for the interactive and usually undirected search for structures and trends.

[Keim et al., 2006]

see also: Visual Analysis, Visual Presentation.

Main characteristics:

• main purpose: support knowledge crystallization / gain insight
• goal is unclear / not clearly defined
• user has no clear question / hypothesis
• undirected
• user is driving force
• highly interactive
• versatile / open-ended
• user: explore

Exploration vs. Presentation [Fisher, 2010]:

	Exploration	Presentation
Characteristics	Data is surprising. Data may have outliers. Data is likely to move unpredictably. Viewer controls interaction.	Data is well known to the presenter. Data has been cleaned. Viewer is passive.
Goals/procedures	Analyze multiple dimensions at once. Change mappings many times. Look for trends and holes.	Present fewer dimensions to make a point. Walk through dimensions clearly. Highlight critical points. Group points together to show trends and motion.

References

• [Fisher, 2010] Danyel Fisher, Animation for Visualization: Opportunities and Drawbacks, in: Julie Steele & Noah Iliinsky (eds.), Bautiful Visualization, Chapter 19, p. 329--352, 2010.
• [Keim et al., 2006] Keim, D.A.; Mansmann, F. and Schneidewind, J. and Ziegler, H., Challenges in Visual Data Analysis, Proceedings of Information Visualization (IV 2006), IEEE, p. 9-16, 2006.
• [Kreuseler et al., 2004] Kreuseler, M., Nocke, T., and Schumann, H. A History Mechanism for Visual Data Mining. In Proceedings of the IEEE Symposium on information Visualization (infovis'04) - Volume 00 (October 10 - 12, 2004). INFOVIS. IEEE Computer Society, Washington, DC, 49-56. 2004.
• [Tominski, 2006] Christian Tominski, Event-Based Visualization for User-Centered Visual Analysis, PhD Thesis, Institute for Computer Science, Department of Computer Science and Electrical Engineering, University of Rostock, forthcoming 2006.
• [Tukey, 1977]: John W. Tukey, : Exploratory Data Analysis. Addison-Wesley, 1977.