Scalable Data Management for Future Hardware - Tapa dura

Acerca del autor

Kai-Uwe Sattler is Full Professor for Database and Information Systems at the TU Ilmenau, Germany. He is the author and co-author of more than 300 publications and 9 textbooks in the field of database systems, algorithms, and data structures for data management. He is also the coordinator of the priority program, the results of which are presented in this book.

Alfons Kemper is Full Professor at the Technical University of Munich, Germany, and head of the Chair of Database Systems. His research group explores ways to optimize information systems for transactional and analytical applications to address the perpetual growth of data. He co-leads the development of the HyPer and the Umbra database systems, both internationally recognized as innovative high-performance database technology achievements.

Thomas Neumann is Full Professor at the Technical University of Munich, Germany, and head of the Chair of Data Science and Engineering. His research concentrates on database system development and query optimization. He has built several successful database systems in past, including the HyPer system and the Umbra system, and he has worked extensively on improving database systems design.

Jens Teubner is Full Professor and the head of the Databases and Information Systems Group at TU Dortmund. The focus of his research is the implementation of database systems on modern hardware architectures. He is the founder of Avalanche, a research project at ETH Zürich that has quickly become an international leader in the use of  FPGAs for database tasks.

De la contraportada

This open access book presents the results of the DFG priority program on Scalable Data Management for Future Hardware. It details requirements and solutions of how modern and future hardware architectures can be leveraged to address the challenges in modern data management.

The nine chapters of the book present a wide range of data management architectures in conjunction with current hardware developments, often related to applications in data analytics or machine learning. They cover topics such as hardware-accelerated query or event processing on FPGA, GPU, and multicore CPUs, scalable data management in data center networks or on modern memory and storage technologies, and operating system support.

This book provides researchers in academia and industry with a comprehensive combination of data management, operating systems, distributed systems and computer architecture issues necessary to address the requirements from practice as well as to propel innovative ideas and challenging research questions.