Teaching:TUW - UE InfoVis WS 2007/08 - Gruppe 03 - Aufgabe 1 - Extract

Extract is the last and 7th task of the Visual Information-Seeking Mantra.

The idea is to extract sub-collections and query parameters e.g. to a file to reuse it somewhere else or to exchange it with other users or systems.

The following quotations explain very well why to do extract and offer some possibilities for intended use.

In the process of using information visualization tools, users are frequently engaged in lengthy and complex operations. Information and knowledge that they discover may be important for several different tasks or ongoing work projects. Accordingly, they should be able to extract important findings for use in other computing systems. Extraction can also provide a means of saving work, thereby preventing the need to repeat data manipulations for future projects.

[Craft, Cairns, 2005]

Once users have obtained the item or set of items they desire, it would be useful to be able to extract that set and save it to a file in a format that would facilitate other uses such as sending by email, printing, graphing, or insertion into a statistical or presentation package. An alternative to saving the set, they might want to save, send, or print the settings for the control widgets. Very few prototypes support this action, although Roth's recent work on Visage provides an elegant capability to extract sets of items and simply drag-and-drop them into the next application window.

[Shneiderman, 1996]

To understand the third quotation you will need the following background information. A Degree of Interest (DOI) function, mapping to the unit interval, is generated by this interaction process to describe the interest level for every feature in the data. Non-binary, i.e., fractional DOI-values allow to specify smoothly delimited features through so-called Smooth Brushing, which describes the users interest better in certain circumstances.

The result of all the mapping, exploration, and interaction process is a selection and rating of interesting data sets. It is important that this user effort is not lost afterwards. It can be extracted, saved, and used in other applications. Smooth brushing assigns an interest rate to every data item which is stored in the DOI array. This array is written back to the database, so the process ends where it began.

Other programs, methods or tools can access that DOI rating and benefit from it in different ways. Either as the amount of data shrank because only highly interesting data points have to be analyzed. Or they gain utility because they use the additional DOI information for color coding or highlighting.

However, the aim of the process described here is to gain insight into the data set and use that knowledge to define subsets for further analysis tasks.

[Sahling, 2002]

This figure shows you where the extract is placed in the Knowledge Crystallization Loop.

References

• [Craft, Cairns, 2005] Brock Craft, Paul Cairns. Beyond guidelines: what can we learn from the visual information seeking mantra?. Information Visualisation, 2005. Proceedings. Ninth International Conference on, 110-118 , July 2005.
• [Shneiderman, 1996] Ben Shneiderman, The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proceedings of the IEEE Symposium on Visual Languages, pages 336-343, Washington. IEEE Computer Society Press, 1996. http://citeseer.ist.psu.edu/409647.html
• [Sahling, 2002] Gerald N. Sahling, Interactive 3D Scatterplots - From High Dimensional Data to Insight, Master's thesis, Vienna University of Technology, 2002.
• [Miksch, 2007] Silvia Miksch. LVA Informationsvisualisierung. Institute of Software Technology and Interactive Systems, Vienna University of Technology. October 2007. http://www.ifs.tuwien.ac.at/~silvia/wien/vu-infovis/