Big Data Benchmarking: 5th International Workshop, WBDB by Tilmann Rabl, Kai Sachs, Meikel Poess, Chaitanya Baru,

By Tilmann Rabl, Kai Sachs, Meikel Poess, Chaitanya Baru, Hans-Arno Jacobson

This publication constitutes the completely refereed post-workshop lawsuits of the fifth foreign Workshop on huge facts Benchmarking, WBDB 2014, held in Potsdam, Germany, in August 2014.

The thirteen papers awarded during this e-book have been rigorously reviewed and chosen from a variety of submissions and canopy themes similar to benchmarks standards and recommendations, Hadoop and MapReduce - within the varied context resembling virtualization and cloud - in addition to in-memory, facts iteration, and graphs.

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Www. org/tpch An Approach to Benchmarking Industrial Big Data Applications Umeshwar Dayal(B) , Chetan Gupta, Ravigopal Vennelakanti, Marcos R. com Abstract. Through the increasing use of interconnected sensors, instrumentation, and smart machines, and the proliferation of social media and other open data, industrial operations and physical systems are generating ever increasing volumes of data of many different types. At the same time, advances in computing, storage, communication, and big data technologies are making it possible to collect, store, process, analyze and visualize enormous volumes of data at scale and at speed.

If the goal is query latency reduction, then the target times should drop proportionally with the number of added worker nodes. The scaling overhead is the time wasted while the system stabilizes. For a single phase and a single worker the scaling overhead is the area B in Fig. 2 which we calculate as the difference of the time spent in the measurement part and the target time for the number of queries the system needed to stabilize. The value is then multiplied by the number of active workers in the respective phase.

In: Proceedings of the VLDB Endowment (2009) 4. : Setting the direction for big data benchmark standards. In: Selected Topics in Performance Evaluation and Benchmarking, pp. 1–13 (2013) 5. : From tpc-c to big data benchmarks: a functional workload model. -A. ) WBDB 2012. LNCS, vol. 8163, pp. 28–43. Springer, Heidelberg (2014) 44 D. Vorona et al. 6. : Benchmarking cloud serving systems with YCSB. In: Proceedings of the 1st ACM symposium on Cloud computing - SoCC 2010, p. 143 (2010) 7. : Measuring elasticity for cloud databases.

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