A vector space model for automatic indexing
Communications of the ACM
Embedded Training for Complex Information Systems
ITS '98 Proceedings of the 4th International Conference on Intelligent Tutoring Systems
The Journal of Machine Learning Research
A diary study of task switching and interruptions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
GaP: a factor model for discrete data
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Natural Language Engineering
Conceptual framework for tasks in information studies: Book Reviews
Journal of the American Society for Information Science and Technology
Human Problem Solving
Introduction to Information Retrieval
Introduction to Information Retrieval
Towards a formalization of individual work execution at computer workplaces
ICCS'11 Proceedings of the 19th international conference on Conceptual structures for discovering knowledge
Interaction history visualization
Proceedings of the 30th ACM international conference on Design of communication
A qualitative metasynthesis of activity theory in SIGDOC proceedings 2001-2011
Proceedings of the 30th ACM international conference on Design of communication
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Knowledge work at computer workplaces involves execution of multiple concurrent tasks with frequent task interruptions. The complexity of the resulting work processes makes task externalization a desired goal towards facilitating analysis and support of knowledge work, e.g. by extracting and disseminating best practices. In this paper, we present a task mining method that identifies tasks based on interaction histories. The method generates instances of a semantic hierarchical task model which captures an abstraction of the work processes. A specific characteristic of the method is that it mines tasks based on a combination of semantic and temporal features, extracted from enriched interaction histories. The use of semantic similarity results in a high robustness of the system with respect to task interruption and concurrent task execution. An evaluation of our task mining method based on a study with users executing frequently interrupted tasks is presented. One element of the evaluation is the assessment of different algorithms for semantic similarity computing, namely Term Matching (TM), Vector Space Model (VSM) and Latent Dirichlet Allocation (LDA). For an approach using VSM a precision of 0.83, a recall of 0.76 and a F1-measure of 0.79 is reached.