2022 Fiscal Year Final Research Report

Software Foundation for Interoperability of Autonomic Distributed Data based on Bidirectional Transformation

Research Project

PDF

Project/Area Number	17H06099
Research Category	Grant-in-Aid for Scientific Research (S)
Allocation Type	Single-year Grants
Research Field	Software
Research Institution	National Institute of Informatics
Principal Investigator	Hu Zhenjiang 国立情報学研究所, 大学共同利用機関等の部局等, 特任教授 (50292769)
Co-Investigator(Kenkyū-buntansha)	吉川正俊京都大学, 情報学研究科, 教授 (30182736) 鬼塚真大阪大学, 大学院情報科学研究科, 教授 (60726165) 石原靖哲南山大学, 理工学部, 教授 (00263434) 日高宗一郎法政大学, 情報科学部, 教授 (70321578) 加藤弘之国立情報学研究所, アーキテクチャ科学研究系, 助教 (10321580)
Project Period (FY)	2017-05-31 – 2022-03-31
Keywords	双方向変換 / 相互運用 / 自律分散データ / プログラミング言語
Outline of Final Research Achievements	Big data processing is widely employed in all aspects of our lives. Usually, parts or copies of a huge amount of data are stored in separate locations, and is infeasible to be collected and processed in a centralized manner. We therefore need new software foundations based on which big data can be efficiently analyzed and shared in a distributed way. A highly relevant research area is bidirectional transformations, which provide a reliable mechanism for data synchronization. Despite the potential in solving practical synchronization problems including data interoperability, bidirectional technologies are not widely employed yet, and most applications of bidirectional transformations remain only proof of concept. In this project, we have further developed bidirectional transformation technology in the direction of "high reliability, large scale, and high efficiency" and established software foundation for integration, sharing, and interoperability of autonomous decentralized big data.
Free Research Field	計算基盤
Academic Significance and Societal Importance of the Research Achievements	本研究は自律分散データの効率的な分析、共有、相互運用を行う新しいソフトウェア基盤技術を確立した。これより、クラウド環境等に置かれた無数のデータから新たな知見を得るというデータの一元管理という考え方を逆転し、データを集中管理せず「集めない」ことで、秘匿性の高いデータをクラウド上で管理することが許されない一般企業の事情にも対応でき、分散した状態のままでデータを活用するとともに分散データ間の整合性が保証できる。