Bidirectional Collaborative Data Management: Collaboration Frameworks for Decentralized Systems - Rilegato

Rilegato

ISBN 10: 9819764289 ISBN 13: 9789819764280

Casa editrice: Springer-Nature New York Inc, 2024

Riassunto

This book summarizes the results of solving the two issues from a 5-year national project in Japan, called Bidirectional Information Systems for Collaborative, Updatable, Interoperable, and Trusted Sharing (BISCUITS) since 2017, with researchers from the National Institute of Informatics, Osaka University, Kyoto University, Nanzan University, Hosei University, Tohoku University, and University of Tokyo. It provides a big picture of the research results, insights, and the new perspectives achieved during the project, paving the way for future further investigation.

Along with the continuous evolution of data management systems for the new market requirements, we are moving from centralized systems, which had often led to vast and monolithic databases, toward decentralized systems, where data are maintained in different sites with autonomous storage and computation capabilities. A common practice is the collaboration or acquisition of companies: there is a large demand for different systems to be connected to provide valuable services to users, yet each company has its own goal and often builds its own applications and database systems independently without federating with others. As a result, we need to construct a decentralized system by integrating the independently built databases through schema matching, data transformation, and update propagation from one database to another.

There are two fundamental issues with such decentralized systems, local privacy and global consistency. By local privacy, the owner of the data stored on a site may wish to control and share data by deciding what information should be exposed and how its information should be used and updated by other systems. By global consistency, the systems may wish to have a globally consistent view of all data, integrate data from different sites, perform analysis through queries, and update the integrated data.

Su questo libro

Informazioni sull?autore

Zhenjiang Hu is the dean and chair professor of the School of Computer Science at Peking University. He obtained his B.S. and M.S. degrees from Shanghai Jiao Tong University in 1988 and 1991, respectively, and his Ph.D. from the University of Tokyo in 1996. His primary research interests lie in programming languages and software engineering, with a focus on functional programming, bidirectional programming, and secure system software. He is a fellow of the Japan Federation of Engineering Societies (JFES), a fellow of IEEE, a member of the Engineering Academy of Japan, and a member of the Academia Europaea.

Makoto Onizuka is a professor at Graduate School of Information Science and Technology, Osaka University. He is the leader of Big Data Engineering Laboratory and conducts research on graph mining algorithms and AI-driven database query optimization techniques. Prior to joining Osaka University, he worked at Nippon Telegraph and Telephone Corporation (NTT) for more than 20 years being served as a distinguished technical member from 2010 to 2014. He also worked as a visiting scholar at the University of Washington from 2000 to 2001. He developed research prototype systems and some of them were used in production: Object-relational main memory database system (LiteObject), XML database system (pgBoscage), XML stream engine (XMLToolkit), Common IT Bases over Cloud Computing at NTT (CBoC type2), and graph mining techniques (Grapon).

Masatoshi Yoshikawa is the dean of the School of Data Science at Osaka Seikei University. He is a professor emeritus at Kyoto University. He received the B.E., M.E., and Ph.D. degrees from the Department of Information Science, Kyoto University, in 1980, 1982, and 1985, respectively. His major research areas have been databases. His current research topics include privacy protection technologies. He is a fellow of Information Processing Society of Japan (DBSJ), a fellow of the Institute of Electronics, Information, and Communication Engineers (IEICE), a member of the IEEE ICDE Steering Committee, and a member of the IEEE BigComp Steering Committee.

Dalla quarta di copertina

This book summarizes the results of solving the two issues from a 5-year national project in Japan, called Bidirectional Information Systems for Collaborative, Updatable, Interoperable, and Trusted Sharing (BISCUITS) since 2017, with researchers from the National Institute of Informatics, Osaka University, Kyoto University, Nanzan University, Hosei University, Tohoku University, and University of Tokyo. It provides a big picture of the research results, insights, and the new perspectives achieved during the project, paving the way for future further investigation.

Along with the continuous evolution of data management systems for the new market requirements, we are moving from centralized systems, which had often led to vast and monolithic databases, toward decentralized systems, where data are maintained in different sites with autonomous storage and computation capabilities. A common practice is the collaboration or acquisition of companies: there is a large demand for different systems to be connected to provide valuable services to users, yet each company has its own goal and often builds its own applications and database systems independently without federating with others. As a result, we need to construct a decentralized system by integrating the independently built databases through schema matching, data transformation, and update propagation from one database to another.

There are two fundamental issues with such decentralized systems, local privacy and global consistency. By local privacy, the owner of the data stored on a site may wish to control and share data by deciding what information should be exposed and how its information should be used and updated by other systems. By global consistency, the systems may wish to have a globally consistent view of all data, integrate data from different sites, perform analysis through queries, and update the integrated data.

Dettagli dell'edizione

• Editore: Springer-Nature New York Inc
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 9819764289
• ISBN 13: 9789819764280
• Rilegatura: Copertina rigida
• Numero di pagine: 125
• Redattore: Hu Zhenjiang, Onizuka Makoto, Yoshikawa Masatoshi
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