TITLE

Machine Learning based Work Task Classification

AUTHOR(S)
Granitzer, Michael; Rath, Andreas S.; Kröll, Mark; Seifert, Christin; Ipsmiller, Doris; Devaurs, Didier; Weber, Nicolas; Lindstaedt, Stefanie
PUB. DATE
October 2009
SOURCE
Journal of Digital Information Management;Oct2009, Vol. 7 Issue 5, p305
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Increasing the productivity of a knowledge worker via intelligent applications requires the identification of a user'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.
ACCESSION #
46794109

 

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