TaskTracer: a desktop environment to support multi-tasking knowledge workers Export

@inproceedings{dragunovEtAl2005:taskTracer, 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.}, address = {New York, NY, USA}, author = {Dragunov, Anton N. and Dietterich, Thomas G. and Johnsrude, Kevin and McLaughlin, Matthew and Li, Lida and Herlocker, Jonathan L.}, booktitle = {IUI '05: Proceedings of the 10th international conference on Intelligent user interfaces}, citeulike-article-id = {233531}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1040855}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1040830.1040855}, doi = {10.1145/1040830.1040855}, isbn = {1-58113-894-6}, keywords = {aaai05, context\_aware, context\_switching}, location = {San Diego, California, USA}, pages = {75--82}, posted-at = {2009-06-11 06:45:01}, priority = {2}, publisher = {ACM}, title = {TaskTracer: a desktop environment to support multi-tasking knowledge workers}, url = {http://dx.doi.org/10.1145/1040830.1040855}, year = {2005} }

TY - CONF ID - dragunovEtAl2005:taskTracer L3 - citeulike-article-id:233531 N2 - 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. CY - San Diego, California, USA SN - 1-58113-894-6 AD - New York, NY, USA EP - 82 TI - TaskTracer: a desktop environment to support multi-tasking knowledge workers PB - ACM T2 - IUI '05: Proceedings of the 10th international conference on Intelligent user interfaces SP - 75 KW - aaai05 KW - context_aware KW - context_switching AU - Dragunov, Anton AU - Dietterich, Thomas AU - Johnsrude, Kevin AU - McLaughlin, Matthew AU - Li, Lida AU - Herlocker, Jonathan PY - 2005/// UR - http://dx.doi.org/10.1145/1040830.1040855 ER -