BUILDING USER TASK SPACE BY MINING TEMPORALLY PROXIMATE DESKTOP ACTIONS
Abstract
Awareness of the user's current context, that is, the task the user resides in, can allow for various ways of supporting their work. In this article we present a novel approach for mining user's regular tasks on the basis of temporal proximity of users’ desktop actions without user intervention in a completely automatic, unobtrusive, and unsupervised manner. The proposed method is based on time-based clustering of a user's desktop actions and, in contrast to previous approaches, it does not enforce fixed time constraints on the mined actions, and typical transition times, when they exist, emerge from the logged activity data for that specific user. The performance of our technique was evaluated on a large data set of 724 days of desktop work by five knowledge workers. The results showed that our approach performed very well and was able to cluster those temporally proximate user actions that commonly used fix time window techniques fail to deal with. Notably, the main improvement is in the recall of the tasks, where our approach recalled 10.18% more actions than the predefined time window technique.
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The user interface used to collect ground truth for task switches and types resembles the one by Mirza et al.
“The user interface we used to collect the ground truth for task switches and types resembles the one by Mirza et al. [15], [25], [33].”
From Detecting Developers' Task Switches and Types (2022) · cites this paper as [25]
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Detecting Developers' Task Switches and Types
Meyer, A. N., Satterfield, C., Zuger, M., Kevic, K., Murphy, G. C., Zimmermann, T., & Fritz, T. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.2984086 · Cited by 14 · see citing works
“Based on previous work [2], [6], [23], [24], [25], [26], we defined a task as a well-defined work assignment with a specific goal that people divide their work into, such as fixing a bug, or preparing for a team-meeting.”
A Study on Automatic Classification of Users’ Desktop Interactions
Mirza, H. T., Chen, L., Hussain, I., Majid, A., & Chen, G. (2015) · Cybernetics and Systems · doi:10.1080/01969722.2015.1012372 · Cited by 8 · see citing works
Self-citation
- Authors
- Mirza, H. T., Chen, L., Majid, A., & Chen, G.
- Affiliations
- Zhejiang University (CN)
- Corresponding author
- Chen, Ling
- Venue
- Cybernetics and Systems
- Publisher
- Informa UK Limited
- Published
- 2011-11
- Volume
- 42
- Issue
- 8
- Pages
- 585-604
- ISSN
- 0196-9722 (ISSN-L)
- 1087-6553
- OpenAlex
- W2037044981
- Cited by
- 2
- References
- 29
- Citations by year
- 2020: 1 · 2015: 1
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cybernetics & Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Social Sciences › Decision Sciences › Information Systems and Management › Personal Information Management and User Behavior
- Keywords
- temporal proximity, user context awareness, unsupervised learning, knowledge workers, activity logging, task identification
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