Group feature extraction based on matrix factorization from long-range office-logging data

Abstract

To increase the productivity of knowledge workers, it is necessary to manage their organization so that they are motivated to collaborate with each other for their synergy. However, it is difficult for managers to grasp the explicit interactions of workers in the organization all the time. Owing to advanced communications technology, and the reduced size and improved capabilities of computers, we are able to record group behaviors as logging data in the office. The aim of this study is to extract features of group behavior from long-range office-logging data. We apply principal component analysis to the data matrix whose element is the mean travel velocity calculated from an individual’s trajectory per day. The results demonstrate the feasibility of our approach, since nontrivial informative group features can be extracted.