Visual Causality Exploration for Scientific Discovery

Koji Koyamada

Academic Center for Computing and Media Studies, Kyoto University, Japan

Abstract

Recently, big data is hot topic and trends that nearly everyone follows, analyzes and celebrates for its unlimited power. Everysecond, unlimited unstructured data is generated by users with clicks, typing and browsing. As big and open data, we can pick up a large dataset from scientific research, social media and public government such as weather, GPS,Census, security and healthcare.

In the big data era, it is expected that every citizencan access the open data and participate in a scientific research. For realizing such a data science, visualization techniques will play an importantrole since they will enable the big data to be transmitted to the brain efficiently.  

It is highly expected for visualization to facilitate an exploration of a causality in a data science field. Although it is possibleto calculate a correlation between data items (variables) by using a statisticmethod, the causality is a feature which domain experts can clarify by making the best of their professional knowledge. There are several examples of illogically inferring causation from correlation. That is why a visualization plays an important role in the scientific discovery.

In this talk, we would like to introduce our activities on visual causality exploration for scientific discovery. The first shows an interactive specification of a latent variable which explains several observable variables by using a causality graph in a phenotypic expression network. The second example explains an interactive exploration of a causality between two time-varying variables defined on computational grids.  

Curriculum Vitae

Koji Koyamada is currently a professor at the Academic Center for Computing and Media Studies, Kyoto University, Japan. His research interest includes modeling & simulation and visualization.      

He is an associate member of the Science Council of Japan, a former president of the Visualization Society Japan, and a former president of Japan Society of Simulation Technology. He received the IEMT/IMC outstanding paper award in 1998, the VSJ contribution award in 2009 and the VSJ outstanding paper award in 2010.      

He received his B.S., M.S. and Ph.D. degrees in electronic engineering from Kyoto University, Japan in 1983, 1985 and 1994, respectively, and worked for IBM Japan from 1985 to 1998. From 1998 to 2001 he was an associate professor at the Iwate Prefectural University, Japan. From 2001 to 2003, he was an associate professor at Kyoto University, Japan.