FRINK Linked Data Fragments server

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• W2128939592 abstract "The requirements for storage space and computational power of large-scale applications are increasing rapidly. Clusters seem to be the most attractive architecture for such applications, due to their low costs and high scalability. On the other hand, smart disk systems, with their large storage capacities and growing computational power are becoming increasingly popular. In this work, we compare the performance of these architectures with a single host-based system using representative queries from the Decision Support System (DSS) databases. We show how to implement individual database operations in the smart disk system and also show how to optimize the execution of the whole query by bundling frequently occurring operations together and executing the bundle in a single invocation. Besides decreasing the overall execution time, operation bundling also offers an easy-to-program and easy-to-use interface to access the data on smart disks. We also present a protocol for minimizing the communication time in the smart disk based system. To measure the response times, we have developed the DBsim, an accurate simulator which can simulate the database operations for the single host-based, cluster-based and smart disk based systems. Using this simulator; we illustrate that the smart disk architecture offers substantial benefits in terms of overall query execution times of the TPC-D benchmark suite. In particular, the average response time of the smart disk architecture for the representative queries from the TPC-D benchmark in our base configuration is 71% smaller than the response time on the single host-based system and 4.2% smaller than the response time on the fastest cluster architecture. We also demonstrate the effectiveness of the operation bundling." @default.
• W2128939592 created "2016-06-24" @default.
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• W2128939592 date "2002-11-08" @default.
• W2128939592 modified "2023-09-29" @default.
• W2128939592 title "Design and evaluation of smart disk architecture for DSS commercial workloads" @default.
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• W2128939592 doi "https://doi.org/10.1109/icpp.2000.876149" @default.
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