KYOTO, Japan--(EON: Enhanced Online News)--Kyoto University (graduate school of informatics, associate professor Ryoichi Shinkuma) and the University of Electro-Communications (graduate school of information systems, associate professor Hiroyuki Kasai), in concert with Kobe Digital Labo Inc. (advanced technology development department head, Kazuhiro Yamaguchi) are conducting joint research into a new generation of network applications based on relational metrics whose goal is to control services and resources according to one’s perceived sense of distance between other people, places and things. Our R&D results are from our ongoing contract research for the National Institute of Information and Communications Technology (NICT): 'New generation network R&D program for innovative network virtualization platform and its application (Sep 2011 ~ Mar 2015)'.
At CEATEC 2013 we will be exhibiting an information delivery app built on potential 'relational metrics' based according to one's 'mobile environment exploit history' and 'social relationships'. We also will exhibit a separate application to visualize the relationships modeled within the app.
■Technology development background
In our society there exists an 'acceptable distance perspective' between us and other people, places, or things. This causes both our interest level and our perception of value towards the other people or things to vary depending on their geographical location and distance. We define this ‘acceptable distance perspective’ as 'relational metrics'. We have developed a technology which utilizes ‘relational metrics’ to model potential relationships between people, places, and things by using physical information and browsing history, including physical movement history and geographic relationship, as well as online information such as SNS connections, to quantify the perceived sense of distance between people, places, and things in order to predict future value.
By using 'relational metrics to evaluate potential connections of which target parties are unaware, we are able to predict the degree of interest they have in other people, places and things and then use this information to allocated services effectively. We anticipate that this technology will be lead to services such as tailored recommendations based on individuals and their circumstances, and content delivery based on forecasting an individual's movements.