Knowledge workers spend the majority of their working hours processing and manipulating information. These users face continual costs as they switch between tasks to retrieve and create information. The TaskTracer project at Oregon State University is investigating the possibilities of a desktop software system that will record in detail how knowledge workers complete tasks, and intelligently leverage that information to reduce costs and errors. Our approach combines human-computer interaction and machine learning to assign each observed action (opening a file, saving a file, sending an email, cutting and pasting information, etc.) to a task for which it is likely being performed. In this talk we report on our experiences developing this software, the application of machine learning in this environment and lessons learned so far.
Dr. Simone Stumpf
Dr. Simone Stumpf joined Oregon State University, USA, at the end of 2004 as a Research Associate, in the main to carry out user studies as part of the TaskTracer project. Previously, she worked at University College London (UCL) on designing knowledge management systems for retail security personnel and evaluating the usability of biometric systems. She received a BSc in Computer Science with Cognitive Science from UCL in 1996 and her PhD from UCL in 2001. She is the organiser for the International Workshop on Theory and Applications of Knowledge Management (TAKMA), held in conjunction with the International Conference on Database and Expert Systems Applications (DEXA).