Download Advancing Big Data Benchmarks: Proceedings of the 2013 by Tilmann Rabl, Nambiar Raghunath, Meikel Poess, Milind PDF

By Tilmann Rabl, Nambiar Raghunath, Meikel Poess, Milind Bhandarkar, Hans-Arno Jacobsen, Chaitanya Baru (eds.)

This ebook constitutes the completely refereed joint court cases of the 3rd and Fourth Workshop on massive facts Benchmarking. The 3rd WBDB used to be held in Xi'an, China, in July 2013 and the Fourth WBDB was once held in San José, CA, united states, in October, 2013. The 15 papers provided during this ebook have been rigorously reviewed and chosen from 33 displays. They specialize in vast information benchmarks; purposes and eventualities; instruments, platforms and surveys.

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Extra resources for Advancing Big Data Benchmarks: Proceedings of the 2013 Workshop Series on Big Data Benchmarking, WBDB.cn, Xi'an, China, July16-17, 2013 and WBDB.us, San José, CA, USA, October 9-10, 2013, Revised Selected Papers

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The code of inputFormat and outputFormat of Pegasus are modified to support sequenceFile format and compression options. The workload uses the automatically generated Web data whose hyperlinks follow the Zipfian distribution. Machine Learning. , large-scale machine learning). 7, which is an open source (Apache project) machine learning library. 7. We have developed a random data generator using statistic distributions to generate the input for the K-means Clustering workload. Analytic Query. , OLAP-style analytical queries).

Investigating, modeling, and ranking interface complexity of Web services on the World Wide Web. In: Proceedings of the 6th World Congress on Services, SERVICES-1, Miami, Florida (2010) 21. : ICOMC: invocation complexity of multi-language clients for classified Web services and its impact on large scale SOA applications. In: Proceedings of the International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT, Hiroshima, Japan (2009) 22. : YCSB++: benchmarking and performance debugging advanced features in scalable table stores.

It is desirable to run experiments for shorter durations than Δ to identify a region in the search space that establishes the true SoAR of a data store. Ideally, the duration of an experiment should be the smallest possible value, δ, that reflects the behavior of a data store as if the experiment was running for Δ time units. The ideal δ is both data store and workload dependent and can be analyzed in a pre-processing step, prior to the rating process. This step involves multiple experiments issuing the given workload against the data store to select the smallest duration that results in a steady system behavior defined as one whose resource utilization and observed throughput do not change in time.

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