The DOI matches its Crossref record; 21 of the 22 references it cites were found in a registry or an index.
TaskTracer: a desktop environment to support multi-tasking knowledge workers
Abstract
This paper reports on TaskTracer --- a software system being designed to help highly multitasking knowledge workers rapidly locate, discover, and reuse past processes they used to successfully complete tasks. The system monitors users' interaction with a computer, collects detailed records of users' activities and resources accessed, associates (automatically or with users' assistance) each interaction event with a particular task, enables users to access records of past activities and quickly restore task contexts. We present a novel Publisher-Subscriber architecture for collecting and processing users' activity data, describe several different user interfaces tried with TaskTracer, and discuss the possibility of applying machine learning techniques to recognize/predict users' tasks.
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“While previous approaches, such as TaskTracer [7], Scalable Fabric [78], GroupBar [9] and Mylyn [6], allow the capturing and presentation of task context, they require the user to manually group related artifacts or manually state the start and end of a task, thus, reducing chances of long-term adoption.”
From Detecting Developers' Task Switches and Types (2022) · cites this paper as [7]
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“The approaches Mylyn by Kersten and Murphy [6], and TaskTracer by Dragunov et al. [7], both explicitly capture task context by automatically recording user interactions within the IDE or the desktop environment respectively, given the user manually indicates the start and end of a task.”
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Metadata
- Authors
- Dragunov, A. N., Dietterich, T. G., Johnsrude, K., McLaughlin, M., Li, L., & Herlocker, J. L.
- Affiliations
- Oregon State University (US)
- Venue
- Proceedings of the 10th international conference on Intelligent user interfaces
- Publisher
- ACM
- Published
- 2005-01-10
- Pages
- 75-82
- OpenAlex
- W2144266514
- Cited by
- 240
- References
- 22
- Citations by year
- 2026: 6 · 2025: 1 · 2024: 4 · 2023: 2 · 2022: 4 · 2021: 3 · 2020: 6 · 2019: 7 · 2018: 3 · 2017: 7 · 2016: 7 · 2015: 6 · 2014: 7 · 2013: 16 · 2012: 17
- FWCI
- 38.92
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Intelligent User Interfaces (IUI) (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Personal Information Management and User Behavior
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- knowledge workers, multitasking, process reuse, user activity monitoring, user interface, task identification, task prediction, machine learning
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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