Large displays are becoming increasingly pervasive. Larger screen size provides an opportunity for users to see more information simultaneously, but at the cost of managing a larger amount of screen space, which is a great burden declining task performance and user experience. User would do/feel better if this burden could be takeover by the computer itself employing techniques that automate the management of screen space. Some studies on automatic window management have been carried out with some success. However, they mainly focus on utilization of empty screen space and/or overlap elimination while ignore preservation of the mental map of users, which tends to cause user confusion and disorientation in practical use. In this chapter, an empirical model is proposed to identifying the degree of mental map preservation for a window layout rearrangement. Furthermore, a method combining high-level window importance with a genetic multi-objective optimization algorithm is presented to generate recommended window layouts featuring a tradeoff among several conflicting goals: (1) better usage of screen space, (2) lower degree of window overlaps, and (3) better mental map preservation. Results suggest that the method is capable of generating suitable window layouts for users and takes a key step toward developing an automated windows manager.
Visual Analytics on Large Displays: Exploring User Spatialization and How Size and Resolution Affect Task Performance
