This book explores the employment of market mechanisms for data-interactive innovations. Based on the concept of innovators' marketplaces the book introduces a new concept of 'data jackets' to enable analysis of what kind of data exist, where they are located, and what kind of information they hold, even if the contents of data cannot be made publicly available.The book presents the concept of a marketplace for data in the case of data-interactive innovations. It introduces the marketplace as a platform for value-based exchange of data and - based on the idea of the innovators' marketplace - explains how data jackets can be utilized independently from the actual contents of the data. Specific chapters deepen the understanding of variables, constraints and intentions as constituent parts of data jackets, and the extension to variable quest, a process towards the design of data. A number of case studies showcases how the methods and processes presented can be employed in real-life contexts. Finally the authors present some extensions of the concept for web-based IMDJ and connections to business information system and an outlook.
Author(s): Teruaki Hayashi, Yukio Ohsawa
Series: Understanding Innovation
Publisher: Springer
Year: 2022
Language: English
Pages: 246
City: Cham
Preface
Acknowledgments
Contents
Chapter 1: Introduction: Why A Market of Data? A Solution for Innovations
1.1 Data: Have We Got It Right?
1.2 Data Marketplace: A Platform for Value Exchange and Innovation
1.3 Interactions of Stakeholders in Markets: Are They Toward Innovations?
1.4 Organization of This Book
References
Chapter 2: Data-Interactive Innovations as the Heart of Market of Data (MoDAT)
2.1 Innovation as a Process of Trans-Dimensional Communication
2.2 Three Reasons for Asking Why and How
2.2.1 Reason 1: Data-Interactive Innovation as Design Communication
2.2.2 Reason 2: Design Communication Requires Two Kinds of Questions
Divergent-Convergent Inquiry-Based Design Thinking Model
Amplex-Limit Model
Conceptual Exploration Through Structured Inquiry and Reframing (ConExSIR)
2.2.3 Reason 3: Externalization of Tsugoes Network for Considering Useful Data
2.3 Creativity
2.4 From Chance Discovery to Innovators´ Marketplace on Data Jackets (IMDJ)
2.5 The Roadmap for Interactive Innovation with the Market of Data
2.6 From the Past to the Present of Data Marketplace
2.6.1 Data Marketing Businesses: An Overview
2.6.2 Survey on Data Marketing Businesses
2.6.3 Data Market Business Trends in Countries/Regions
Japan
United States
European Union (EU)
China
2.6.4 Data Marketplaces in the Future
2.7 Summary
References
Chapter 3: Tools for Activating Data Marketplace (1)
3.1 Data Jackets for Representation of One´s Own Knowledge About Data
3.2 Method of Innovators´ Marketplace on Data Jackets
3.2.1 Innovators´ Marketplace as the Basis for IMDJ
3.2.2 The Procedure of IMDJ
3.3 The Formal Definition of DJs and Their Role in Satisfying Requirements
3.4 Analogy Applied on the Predicate-Logic Representations
3.5 Case Summaries of IMDJ
3.6 Action Planning
3.6.1 Scenario Generation to Activate Actions
3.6.2 Outline of Action Planning
3.6.3 Action Planning Sheet
3.6.4 Action Planning Process with Example
3.7 Extension of IMDJ by Coupling with Living Lab
3.7.1 The Process of IMDJ with LL
3.8 Summary
References
Chapter 4: Tools for Activating Data Marketplace (2)
4.1 Data Jacket Store: Data Platform with Retrieval System
4.1.1 Why Is It Difficult to Discover Data Related to Our Interests?
4.1.2 Reuse of Knowledge for Data Utilization
4.1.3 Implementation
4.1.4 Performance
4.2 Variable Quest: Inferring Method of Variables
4.2.1 Design of Data
4.2.2 Data Similarity and Co-occurrence of Variables
4.2.3 Estimation Process of Variables
4.2.4 Implementation and Performance
4.3 TEEDA: A Platform for Call for Data and Matching
4.3.1 Call for Data and Matching
4.3.2 Data Requests and Providable Data
4.3.3 Implementation of TEEDA as a Web Application
4.3.4 Use Case and the Findings Using TEEDA
4.4 Web-Based IMDJ: Accelerating Communications for Innovation
4.4.1 Why Discuss Data Utilization on the Web?
4.4.2 Ordinal IMDJ Process
4.4.3 How to Use Web IMDJ
4.4.4 Face-to-Face Versus Online Discussions
4.4.5 Experiments and Discussion
4.5 Human Resource Finder
4.5.1 Stakeholders in Scenarios
4.5.2 Knowledge Structuring of Scenarios
4.5.3 Dataset Extension and Relationship Estimation
4.5.4 Implementation and Use Cases
4.6 Summary
References
Chapter 5: Knowledge Structuring and Acquisition for Data Exchange
5.1 Knowledge Representation for Data Utilization
5.1.1 Knowledge Structuring for Data Utilization
5.1.2 A Case of Open Data Exchange Platform: Yokohama and Kawasaki Cities
5.1.3 A Case of Discussion on COVID-19 Pandemic
5.1.4 How to Use Structured Data-Related Knowledge in the Future
5.2 Datascape Analysis for Understanding the Data Exchange Ecosystem
5.2.1 Our Motivation and Datascape Analysis
5.2.2 Variable Characteristics: Frequency and Distribution
5.2.3 Data Network with Shared Variables
5.2.4 Data Combinability with Centrality Values
5.2.5 Suggestions for Data Platforms Regarding Data Economy Policies
5.3 Stakeholder-Centric Value Chain of Data Ecosystem
5.3.1 Understanding the Data Exchange Ecosystem from Business Players and Their Roles
5.3.2 Stakeholder-Centric Value Chain of Data
5.3.3 Structural Characteristics of the Data Exchange Ecosystem
5.3.4 Knowledge Structuring and Workshop-Styled Usage of SVC
5.4 Summary
References
Chapter 6: Case Studies of Innovators´ Marketplace on Data Jackets
6.1 The Triple as the Representation of Case Summaries in IMDJ
6.2 Streetlight Visualizer: Safety of Roads for Pedestrians
6.3 Improvement of Working Conditions
6.4 Machine Learning Applied to Physics via IMDJ
6.5 A New Problem of Data Science Externalized via Other IMDJ Sessions
6.6 Do Methods for Sequence Analysis Work for Change Explanation?
6.7 New Techniques for Change Explanation: Fruits of IMDJ
6.7.1 New Technique 1: Tangled String
Applied Case of TS (1): Finding Triggers of Contextual Shifts in Discourse
Applied Case of TS (2): Finding Noteworthy Body Movements
Applied Case of TS (3): Features in the Changes in Markets
6.7.2 New Technique 2: Graph-Based Entropy
6.8 Analogy from One Success Case to Another: Toward the Explanation of Earthquake Precursors
6.9 Data Co-creation Project in Tokyo Marunouchi Area (2018-2019)
6.10 Cross-Media Trend and Promotion Measurement Framework
6.11 Data Jackets in the Data Society Alliance in Japan
6.12 Summary
References
Chapter 7: Conclusions
Index
