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Titlebook: Big Data Benchmarking; 5th International Wo Tilmann Rabl,Kai Sachs,Hans-Arno Jacobson Conference proceedings 2015 Springer International Pu

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Review of Semiconductor Physics,s processes and their involved master and transactional data objects. The interactions are correlated in controlled ways to enable non-uniform distributions for data and relationships. For benchmarking data integration, the generated data is stored in two interrelated databases. The dataset can be a
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https://doi.org/10.1007/978-0-387-76635-5ring to the new standards requires industry commitment and collaboration. In this paper, we discuss the problems we observe in the current practices of benchmarking, and present our proposal for bringing standardization in the SQL-on-Hadoop space.
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Springer Optimization and Its Applicationsmpanies have no need to reveal sensible data but yet developers have access to important development artifacts. We demonstrate our approach by generating a customized test set with medical information for developing SAP’s healthcare solution.
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Benchmarking SQL-on-Hadoop Systems: TPC or Not TPC?ring to the new standards requires industry commitment and collaboration. In this paper, we discuss the problems we observe in the current practices of benchmarking, and present our proposal for bringing standardization in the SQL-on-Hadoop space.
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0302-9743 ed from numerous submissions and cover topics such as benchmarks specifications and proposals, Hadoop and MapReduce - in the different context such as virtualization and cloud - as well as in-memory, data generation, and graphs..978-3-319-20232-7978-3-319-20233-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
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https://doi.org/10.1007/978-1-4757-3745-5influence the performance of CPU bound and I/O bound workloads. Based on our observations, we identify three important factors that should be considered when configuring and provisioning virtualized Hadoop clusters.
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