IEEE International Conference on Big Data
The 4th International Workshop on Cross-disciplinary Data Exchange and Collaboration (CDEC2021)
–System Modeling and Data Origination for Social Implementation–
December 15th-18th, 2021 in conjunction with IEEE BigData 2021, Orlando, FL, USA Online conference
Scope
The recent social movement of big data and artificial intelligence has resulted in a tremendous increase in the importance of data. In view of these expectations, there are the externalizations of interdisciplinary issues. Many papers about data utilization have been published, and several approaches for analyzing data have been shared widely. However, there are only a limited number of studies on the process of cross-disciplinary data exchange and collaboration and its ecosystem. Since this process encompasses various activities of different stakeholders, it is difficult to evaluate the patterns or processes quantitatively. Moreover, in the data market, there are not only big data but also small data necessary for decision making. It is essential to discuss the dynamics of such a network of heterogeneous data in different fields.
To address these gaps, we propose to hold a fourth edition of the session to discuss the processes and interactions among data, humans, and society –Cross-disciplinary Data Exchange and Collaboration (CDEC). The topics taken up at CDEC will involve practical issues such as the analytical tasks performed using data, solutions for challenging social issues, and cross-disciplinary data collaboration and its process. Our workshop will target not only cleanly formatted homogenous data, but also heterogeneous data that affect human behaviors, thoughts, and intentions across different domains. We will also focus on a discussion to obtain tacit knowledge of artificial intelligence and data mining by analysis and synthesis. In addition to these research fields, we will attempt to take a cognitive approach toward observing the processes of knowledge discovery and data exchange. It is expected that conflicts and inconsistencies may arise owing to differences in opinion when stakeholders from different knowledge domains have discussions on data-driven decision making. We believe that this special session focusing on the process of cross-disciplinary data exchange and collaboration will have great significance, not only on academia, but also on the society as a whole.
This year, we are planning to expand the scope beyond the results of data mining, and present, share, and discuss the entire process from data design to analysis by setting the theme as “System Modeling and Data Origination for Social Implementation.”
Moreover, we believe that this workshop is a natural extension of the International workshop on the Market of Data (MoDAT), which was conducted as a series at ICDM from 2013 to 2017. In the MoDAT workshops, we discussed approaches toward designing the data market, and proposed solutions leading to productive actions in businesses and sciences spanning the industrial, political, and educational sectors. In the CDEC workshop, we will also discuss the practical feasibility of these applications based on the discussions at MoDAT. We included MoDAT in the Topics of this proposal.
Topics
We call for anyone interested in the following topics of Cross-disciplinary Data Exchange and Collaboration.
Data Mining Application Areas:
- Statistical Graphics and Mathematics
- Financial, Security and Business
- Physical Sciences and Engineering
- Earth, Space, and Environmental Sciences
- Geographic/Geospatial/ Terrain Data Mining
- Text, Documents and Software
- Social, Ambient and Information Sciences
- Multimedia (Image/Video/Music) Mining
Case Studies on Data Exchange and Collaboration:
- Methods for data evaluation and utilization
- Data management and curation
- Risks, limitations, and challenges of Data Exchange
- Trust, resilience, privacy and security issues
- Design of Data
Empirical And Comprehension Focused Data Mining:
- Modeling of Machine Learning for Social Data
- Data Mining and Machine Learning Methods Based on Empirical Knowledge
- Ontology and Dictionary
- Business Efficiency
- Cognition and Perception Issues
- Natural Language Processing Text mining
- Retrieval/recommender systems
Data Focused Visualization Research:
- High-Dimensional Data, Dimensionality
- Reduction, and Data Compression
- Multidimensional Multi-Field, Multi-Modal, Multi-Resolution and Multivariate Data
- Causality and Uncertainty Data
- Time Series, Time Varying, Streaming Data
- Point-Based Data and Large Scale Data
Data Focused Cognitive Research:
- Human-Computer Interaction, Cognitive Science, and Behavioral Science and Modeling, including quantitative and qualitative results
- Theoretical models, technological advances and experimental methods of Human-Computer Interaction, Cognitive Science, and Behavioral Science and Modeling
Market of Data
- Process and Technologies for Data Exchange
- Representation of Knowledge and Requirements
- Pricing and Evaluating Mechanism of Data
- Design of Data Platform
- Data Acquisition, and Sensors
Key Dates
Full paper submissions: September 17, 2021Full paper submissions: September 26, 2021 (extended)- Full paper submissions: October 10, 2021 (extended)
- Paper notification: October 22, 2021
- Camera-ready deadline and copyright forms: November 15, 2021
- Conference dates: December 15-18, 2021
Submission
Paper submissions should be limited to a maximum of ten (10) pages, in the IEEE 2-column format (link), including the bibliography and any possible appendices. Submissions longer than 10 pages will be rejected without review.
Note that all accepted papers will be included in the Proceedings volume in the IEEE Xplore Digital Library. Therefore, papers must not have been accepted for publication elsewhere or be under review for another workshop, conferences or journals.
The submission guidline has been confirmed by the IEEE BigData chairs. Please read carefully the following instructions.
- 1. Papers with more than 8 pages will need to pay extra page fee. The allowed max page is 10 pages.
- 2. The extra page fee is 100US$ per page.
Submission page has been closed. Thank you for your contribution!
Schedules and Invited Speaker
Go to Schedule
Smile Spirits (Representative)
ABSTRACT
It has been a long time since data analysis and video analysis have been used in sports as well. Especially in recent years, data analysis using sensor technology has been performed to help prevent injuries and adjust practice menus. Although the technology has evolved and the price of equipment has dropped compared to 10 years ago, it has not been popularized and is currently available only to top athletes such as professional sports and national athletes. In this session, we will explain how data analysis and video analysis are used in sports with examples, and how can we use them for more athletes and sports players? And how is it desirable for athletes to utilize data and video analysis? I would like to make time to think about these things.
BIOGRAPHY
From 1998 to 2008, he participated in competitions around the world as a moguls player. At the same time, he studied sports coaching using video analysis and also served as a private coach for the Korean national team moguls. Currently, I am applying this research to coach various athletes such as soccer, basketball, fencing, and boccia using video analysis. In 2016, using sports data and video analysis, we developed "winning habit coaching" to sharpen the senses while drawing out the independence of athletes.
In recent years, world champions have also been produced, contributing to winning medals at the Tokyo Olympics and Paralympics. In addition, he has been a deputy representative director of the Japan E-Coaching Association, and has been training analysts and analyzing coaches as professional performance analyst since 2007. He has also served as a part-time lecturer at Takushoku University and a part-time lecturer at Kokushikan University for many years, teaching video analysis coaching in lectures and practical training, and contributing to the development of many teachers and coaches.
Organizers
Teruaki Hayashi (PhD)
The University of Tokyo
CV
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
CV
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.
Yoshiaki Fukami (PhD)
Gakushuin University/ Keio University
CV
Yoshiaki Fukami is a Specially Appointed Professor at Department of Management at Gakushuin University, and Visiting Researcher at School of Medicine at Keio University. He worked at Rikkyo Business School (Specially Appointed Associate Professor, 2015-2020). He is a director of The Japan Society for Management Information. He received his Ph. D. (Media and Governance) from Keio University. Fukami’s specialization is in standardization strategy, platform strategy, open collaborative innovation. He has contributed to Open data technical strategy of government at the Vitalizing Local Economy Organization by Open Data & Big Data (a member of Technical Committee), the Committee at the Infrastructure for Multi-layer Interoperability Working group, the Committee at The Research Committee of Scheme for Interoperability of Public-sector Information, and Technical Committee at the Open Data Promotion Consortium. Information-technology Promotion Agency, Japan. He has also contributed to web standardization activities at the World Wide Web Consortium. He was awarded the Young Investigator Award 2018 autumn by Japan Society for Information and Management. He is the author of books; “Utilizing web application for your business” (Discover Twenty-One Publishing, 2010), “Metadata is changing the world” (NTT Publishing, 2009), and chapter; Platform Strategy suited for IoT based technology: How to Develop and Engage Ecosystems, in “Business Design and Business Science” (Soseisha, 2016), Behavior Analysis in Activities for Building Ecosystems: Competitive Strategy of Platforms in Standardizing Processes in “Platform for Emergence” (Nikkei Newspaper Publishing, 2011). He is also the editor of a book; “Technical review of HTML5 to enhance skills for web contents development” (RIC Telecom Publishing, 2012).
Organizing Committee
Note: Since we are contacting to other researchers, more program committees will be added to the list.
Co-chairs
- Teruaki Hayashi, The University of Tokyo, Japan
- Yukio Ohsawa, The University of Tokyo, Japan
- Yoshiaki Fukami, Gakushuin University/ Keio University, Japan
Program Committee
- Akinori Abe, Chiba University, Japan
- Eiji Murakami, Azbil Kimmon Co.Ltd., Japan
- Dominik Ślęzak, Warsaw University, Poland
- Hiroki Sakaji, The University of Tokyo, Japan
- Hiroyasu Matsushima, Shiga University, Japan
- Jun Nakamura, Chuo University, Japan
- Mi-Young Kim, The University of Alberta, Canada
- Naohiro Matsumura, Osaka Univeristy, Japan
- Noriyuki Kushiro, Kyushu Institute of Technology, Japan
- Takumi Shimizu, Keio University, Japan
- Yasufumi Takama, Tokyo Metropolitan University, Japan
- Yuki Hayashi, Chiba University, Japan
Our Past Records
As part of the ICDM (International Conference on Data Mining) workshops, we successfully conducted the 1st and 2nd International Workshop on CDEC at Singapore and Beijing (2018 and 2019). Seven distinguished papers were accepted after a triple blind review process (the acceptance rate: 43%) and presented to an audience of approximately 30 participants in 2018. In 2019, 10 papers have been accepted and been presented (the acceptance rate: 48%) after a triple blind review process. Thanks to the editors of MDPI, a special issue of CDEC was issued in the Information Journal (ISSN 2078-2489) under the section “Information and Communications Technology.” In 2020, we proposed the special session on IEEE BigData2020, and 4 papers have been presented online (the acceptance rate: 40%) after a triple blind review process.
CDEC Series
- CDEC2018 in ICDM at Singapore
- CDEC2019 in ICDM at Beijing, China
- CDEC2020 in IEEE BigData (Online conference)
Moreover, we have had experience in initiating and conducting the MoDAT workshop at ICDM from 2013 to 2017. According to the workshop committee of ICDM 2013, MoDAT was reported to the steering committee as the best of all the 17 workshops in the conference. Moreover, MoDAT was selected as one of the nine full day workshops from among 30 proposals for ICDM 2014 according to Wei Wang and Zhi-Hua Zhou, the workshop chairs of ICDM 2014. MoDAT was included both in ICDM 2015 and ICDM 2017 as a full day workshop. Furthermore, we organized and are organizing the following workshops/sessions about data-based approaches to chance discovery and MoDAT: Chance Discovery and Data Synthesis (more than 30 international workshops and symposia, including IJCAI 2015, IJCAI 2011, ECAI 2016, ECAI 2011, ICDM 2010, ECAI2004, KES2000-2019, AAAI Spring Symposia 2001).
Contact
Dr. Teruaki Hayashi (co-chair)
Email: hayashi -at- sys.t.u-tokyo.ac.jp