2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops) 2010
DOI: 10.1109/percomw.2010.5470663
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Optimizing meeting scheduling in collaborative mobile systems through distributed voting

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Cited by 2 publications
(8 citation statements)
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“…" We also present SelfPlanner 2.7, a prototype electronic calendar application that utilizes the research produced in this thesis, to produce a prototype for our vision of the electronic calendar of the future. SelfPlanner, which can be used either as a stand-alone electronic calendar application, 5 or through an Application Programming Interface (API) for use by other electronic calendar applications and programs. It greatly simplifies the problem model (as presented in Section 1.1), and provides a higher-level view of it to the user (both the end-user using its client 6 and the programmer using its API).…”
Section: Planning Individual Activities Through An Intelligent Calendarmentioning
confidence: 99%
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“…" We also present SelfPlanner 2.7, a prototype electronic calendar application that utilizes the research produced in this thesis, to produce a prototype for our vision of the electronic calendar of the future. SelfPlanner, which can be used either as a stand-alone electronic calendar application, 5 or through an Application Programming Interface (API) for use by other electronic calendar applications and programs. It greatly simplifies the problem model (as presented in Section 1.1), and provides a higher-level view of it to the user (both the end-user using its client 6 and the programmer using its API).…”
Section: Planning Individual Activities Through An Intelligent Calendarmentioning
confidence: 99%
“…To solve CSP problems, we combine search methods using heuristics (such as picking the most constrained variable, the least constrained etc. ), 5 to partition the search space into smaller sub-spaces, with constraint propagation to exclude invalid solutions from the search space [119]. To do constraint propagation we have to decide what level of consistency we want to enforce (i.e., node consistency and arc consistency).…”
Section:  Constraint Satisfaction Problemsmentioning
confidence: 99%
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