Information workers are often involved in multiple tasks and activities that they must perform in parallel or in rapid succession. In consequence, task management itself becomes yet another task that information workers need to perform in order to get the rest of their work done. Recognition of this problem has led to research on task management systems, which can help by allowing fast task switching, fast task resumption, and automatic task identification. In this paper we focus on the latter: we tackle the problem of automatically detecting the tasks that the user is involved in, by identifying which of the windows on the user's desktop are related to each other. The underlying assumption is that windows that belong to the same task share some common properties with one another that we can detect from data. We will refer to this problem as the task assignment problem.
To address this problem, we have built a prototype named Swish that: (1) constantly monitors users' desktop activities using a stream of windows events; (2) logs and processes this raw event stream, and (3) implements two criteria of window "relatedness'', namely the semantic similarity of their titles, and the temporal closeness in their access patterns.
We have validated Swish with 4 hours of user data, obtaining task classification accuracies of about 70\%.
We are planning to include Swish in a number of intelligent user interfaces.
SWISH: Semantic Analysis of Window Titles and Switching History, Nuria Oliver, Greg Smith, Chintan Thakkar and Arun C. Surendran. Proceed. of IUI 2006