@article {263,
	title = {An ontology-based approach for detecting knowledge intensive tasks},
	journal = {Journal of Digital Information Management},
	volume = {9},
	year = {2011},
	note = {Cited By (since 1996):2Export Date: 10 July 2014},
	month = {2011},
	pages = {9 - 18},
	abstract = {In the context detection field, an important challenge is automatically detecting the user{\textquoteright}s task, for providing contextualized and personalized user support. Several approaches have been proposed to perform task classification, all advocating the window title as the best discriminative feature. In this paper we present a new ontology-based task detection approach, and evaluate it against previous work. We show that knowledge intensive tasks cannot be accurately classified using only the window title. We argue that our approach allows classifying such tasks better, by providing feature combinations that can adapt to the domain and the degree of freedom in task execution.},
	keywords = {Classification, Context-awareness, Task detection, User context detection, User context ontology},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960684788\&partnerID=40\&md5=2680764b7de9a96a3bfc50e670a65ce8},
	author = {Rath, A.S. and Devaurs, D. and Lindstaedt, S.N.}
}
@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.}
}
