@article {197,
	title = {Machine learning based work task classification},
	journal = {Journal of Digital Information Management},
	volume = {7},
	year = {2009},
	note = {Cited By (since 1996):6Export Date: 10 July 2014},
	month = {2009},
	pages = {305 - 312},
	abstract = {Increasing the productivity of a knowledge worker via intelligent applications requires the identification of a user{\textquoteright}s current work task, i.e. the current work context a user resides in. In this work we present and evaluate machine learning based work task detection methods. By viewing a work task as sequence of digital interaction patterns of mouse clicks and key strokes, we present (i) a methodology for recording those user interactions and (ii) an in-depth analysis of supervised classification models for classifying work tasks in two different scenarios: a task centric scenario and a user centric scenario. We analyze different supervised classification models, feature types and feature selection methods on a laboratory as well as a real world data set. Results show satisfiable accuracy and high user acceptance by using relatively simple types of features.},
	keywords = {Data model, Data structure, Work task classification user interaction},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77953256434\&partnerID=40\&md5=ad1b8dc83a81694afe23a15ad90c66af},
	author = {Granitzer, M. and Rath, A.S. and Kr{\"o}ll, M. and Seifert, C. and Ipsmiller, D. and Devaurs, D. and Weber, N. and Lindstaedt, S.}
}
