Enabling Ad-Hoc Collaboration Through Schedule Learning and Prediction
Abstract
The transferal of the desktop interface to the world at large is
not the goal of ubiquitous computing. Rather, ubiquitous computing
strives to increase the responsiveness of the world at large to the
individual. A large part of this responsiveness is improved
communication with other individuals. In this paper we describe a
system that can enable ad-hoc collaboration between several people
by creating a model of the daily schedules of individuals and by
performing predictions based on this model. Using GPS data we learn
to distinguish locations and track the times that these locations
are visited. In addition, we use Markov models to predict which
locations might be visited next based on the user's previous
behavior.
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