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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 mining 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 based on the knowledge acquired by data mining. Since this process encompasses various activities of different stakeholders, it is difficult to evaluate the patterns or processes quantitatively.
To address this knowledge gap, we propose to hold a second edition of the workshop to discuss data-driven decision making focusing on 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 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 a workshop 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.
As part of the ICDM Workshop in 2018, we successfully conducted the 1st International Workshop on CDEC at Singapore. Seven distinguished papers were accepted after a triple blind review process (total acceptance rate was 43%). In the half-day workshop, 10 presentations (7 research presentations, 2 position talks, and 1 invited talk) were made to an audience of approximately 30 participants. Thanks to the editors of MDPI, a special issue of CDEC was issued in the Information Journal (ISSN 2078-2489) after the conclusion of the workshop under the section “Information and Communications Technology.”
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 “Design, Acquire, and Integrate Data for Valuable Knowledge Discovery.”
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.
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
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