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 third edition of the session to discuss the processes and interactions among data, humans, and society –Data Marketing as 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 special session 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 “Designing Data Exchange Ecosystem for Human-AI Collaborative Society.”

We call for anyone interested in the following topics related to CDEC:

((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, and 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, and 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 in 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