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        Scalable Data Management for Future Hardware

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        Contributor(s)
        Sattler, Kai-Uwe (editor)
        Kemper, Alfons (editor)
        Neumann, Thomas (editor)
        Teubner, Jens (editor)
        Language
        English
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        Abstract
        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.
        URI
        https://library.oapen.org/handle/20.500.12657/98554
        Keywords
        data management; data integration; distributed data management; storage technologies; in-memory database; main-memory database; non-volatile memory
        DOI
        10.1007/978-3-031-74097-8
        ISBN
        9783031740978, 9783031740978, 9783031740961
        Publisher
        Springer Nature
        Publisher website
        https://www.springernature.com/gp/products/books
        Publication date and place
        Cham, 2025
        Imprint
        Springer Nature Switzerland
        Classification
        Databases
        Computer hardware
        Pages
        240
        Rights
        http://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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