In recent years, the increased access to knowledge and data has been changing the way we see the world and the systems that surround human life and society. The recent social movement of big data and artificial intelligence has resulted in a tremendous increase in the importance of data. Moreover, the global violence of the novel coronavirus has affected various industries, and the breakdown between systems has been apparent. To understand and overcome the phenomenon related to this unprecedented crisis, the importance of data exchange and sharing across fields has gained social attention. However, the new issue arising in the corona-related confusion is the discussion to determine what types of data are missing. The current situation is limited to unilateral information provision from data providers, and there has been almost no discussion about creating data of unobserved events and the methodologies for supporting it.
To address these gaps, we propose to hold the session to discuss the processes and interactions among data, humans, and society–the Special Session on Data Origination from Unobserved Events. The data origination is the human-centered approach to design, obtain, and make use of the data by emphasizing the diversity of subjective knowledge to externalize and solve the interdisciplinary issues. The topics taken up at this special session will involve practical issues such as the analytical tasks performed using data, soft computing and pattern recognition, and data origination and its process. Our special session will target not only cleanly formatted homogenous data, but also heterogeneous data that affect human behaviors, thoughts, and intentions across different domains. In addition to these research fields, we will attempt to take a cognitive approach toward observing the processes of data design and acquisition. We believe that this special session will have great significance, not only on academia but also on society as a whole.
We call for anyone interested in the following topics of Data Origination from Unobserved Events.
Data-oriented Application Areas:
Case Studies on Data Origination and its Application:
Data Focused Cognitive Research:
Submitted papers should be original and contain contributions of theoretical, experimental or application nature, or be unique experience reports.
Papers must be submitted within the deadline given and electronic submission in PDF is required.
Papers maximum length is 10 pages. Papers must be formatted according to Springer format (Latex/word) available at: http://www.springer.com/series/11156
All accepted papers fulfilling requirements on quality will be published in the Springer proceedings. It is mandatory that at least one author registers for every paper that is included in the conference proceedings. Proceedings are expected to be published by: Advances in Intelligent and Soft Computing (Springer).
To submit your paper to the special session, please choose "SS1: Data Origination from Unobserved Events."
Teruaki Hayashi (PhD)
The University of Tokyo
Teruaki Hayashi is an Assistant Professor of Systems Innovation affiliated to the School of Engineering at the University of Tokyo, and vice chairman of the Application Committee at the Data Trading Alliance. He received his Ph.D. (Engineering) from the University of Tokyo. Hayashi’s specialization is in knowledge structuring for Data Utilization and Scenario Creation. He developed Data Jacket, a method for summarizing the information in data, as his core research and applied the results to the industry, government, and academia internationally. He was awarded the Dean’s Award by the School of Engineering at the University of Tokyo in 2017, and an Excellence Award at the 23rd Annual Conference of the Japanese Society of Artificial Intelligence in 2018. He is also the co-author of a book “Market of Data” Kindaikagakusha (2017).
Yukio Ohsawa (PhD, Professor)
The University of Tokyo
Yukio Ohsawa is a Professor of Systems Innovation with the School of Engineering at the University of Tokyo. He received a PhD from the School of Engineering at the University of Tokyo (1995). Then, he worked at the School of Engineering Science at Osaka University (research associate, 1995-1999), Graduate School of Business Sciences at the University of Tsukuba (associate professor, 1999-2005), and moved back to the University of Tokyo. In the field of artificial intelligence, he has created a new domain Chance Discovery to discover events with significant impact on decision making. He has delivered many keynote talks about Chance Discovery in conferences such as the International Symposium on Knowledge and Systems Sciences, International Conference on Rough Sets and Fuzzy Sets, Joint Conference on Information Sciences, and Knowledge-Based Intelligent Information and Engineering Systems. Chance Discovery has been embodied as an innovators’ marketplace, a methodology for innovation that is borrowed from principles of market dynamics. His original concepts and technologies have been published as books and monographs by global publishers such as Springer Verlag and Taylor and Francis. The two most important books among these are “Chance Discovery” (2003 Springer, Eric von Hippel gave the opening) and “Innovators’ Marketplace: Using Games to Activate and Train Innovators” (2012 Springer, Larry Leifer gave the opening). He has edited special issues as a guest editor for journals mainly related to chance discovery such as Intelligent Decision Technologies (2016), Information Sciences (2009), New Generation Computing (2003), and the Journal of Contingencies and Crisis Management (2002). He has also published 100 journal papers and made several presentations at conferences. He has initiated symposia and workshops on data-based approaches toward business innovation.
Dr. Teruaki Hayashi (co-chair)
Email: hayashi -at- sys.t.u-tokyo.ac.jp