Integrating Lite-Weight but Ubiquitous Data Mining into GUI Operating Systems
Li Wei (University of California, USA)
Eamonn Keogh (University of California, USA)
Xiaopeng Xi (University of California, USA)
Stefano Lonardi (University of California, USA)
Abstract: Most visualization tools introduced in the literature are specialized for a particular task. In this work, we introduce a novel framework which allows visualization to take place in the background of normal day to day operations of any GUI based operating system such as MS Windows, OS X or Linux. Our system works by replacing the standard file icons with automatically generated icons that reflect the contents of the files in a principled way. We call such icons Intelligent Icons. While there is little utility in examining an individual icon, examining groups of them provides a greater possibility of unexpected and serendipitous discoveries. The utility of Intelligent Icons can be further enhanced by arranging them on the screen in a way that reflects their similarity/differences. We demonstrate the utility of our approach on data as diverse as DNA, text files, electrocardiograms, and Space Shuttle telemetry. In addition we show that our system is unique in also supporting fast and intuitive similarity search.
Keywords: data mining, icon, visualization
Categories: H.3.0, H.3.3, H.3.4