Machine Learning of User Attentions in Sensor Data Visualization

Reviewed
Keita Fujino, Sozo Inoue, Tom Shibata,
EAI International Conference on Mobile Computing, Applications and Services (MobiCASE)
(Not Available)
(Not Available)
125-143
2018-03-01
Osaka
http://mobicase.org/2018/show/home
In this paper, we propose a method to automatically esti- mate important points of large sensor data by collecting users attention points when visualized, and by applying supervised machine learning al- gorithm. For large scale sensor data, it is difficult to find important points just by visualization, because the points are buried in a large scope of visualization. We show the result of the estimation, where the accuracy was over 80% for multiple visualization. We also show the result of re- usability for new type of visualization, which performed still 70-80% of accuracies.