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.
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The last decade has witnessed an unprecedented growth of information, display size and multi-task interfaces. The larger screen size provides an opportunity for users to see more information in more windows at the same time, but at the cost of managing a larger amount of screen space. For example, when using some information-intensive software such as visual analytics and stock market quotation analysis, users often need frequent window operations (i.e., add, move, delete and resize) to obtain the desired information. These operations are not relevant to the task and, as a result, drag users out of their task domain. Users’ mental resources consumed by window operations make no contribution to the task performing. Additionally, when a window operation is completed, a recall process is necessary before users can return into their task context and proceed. This is quite similar to process scheduling performed by computer operating systems. Consequently, such methods of manually managing windows not only induce users’ attention to be interrupted, thereby decreasing their overall task performance (Bly & Rosenberg, 1986; Bury, Davies, & Darnell, 1985; Davies, Bury, & Darnell, 1985; Johnson-Laird, 1985; Kandogan & Shneiderman, 1997; Sandberg-Diment, 1984), but also dissipate valuable mental resources. Research on automatic window layout optimization aiming to reduce the amount of needed window operations throughout the process of task performing is thus of great importance.
Improving the interaction with multi-window interfaces has been a challenge for HCI and interface researchers and some efforts have been made (Beaudouin-Lafon, 2001; Hutchings & Stasko, 2002a, 2002b, 2004; Trivedi, Lai, & Zhang, 2001). They can generally be split into two sub-categories: (1) automatically optimizing window layout, in which interfaces focus on helping users effectively utilize display space usually by maximizing visual scope and minimizing the overlap between windows, and (2) developing more efficient window operations, in which interfaces focus on considering whether the standard window operations could be improved or expanded to better help users efficiently operate the windows in the context of multi-tasks. This study lies in the former sub-category and, specifically, focuses on the integration of automatic optimization of window layout and preservation of users’ mental map as well as their balance.