BigBench

Ghazal, Ahmad; Rabl, Tilmann; Hu, Minqing; Raab, Francois; Poess, Meikel; Crolotte, Alain; Jacobsen, Hans‐Arno

doi:10.1145/2463676.2463712

Cited by 271 publications

(20 citation statements)

References 13 publications

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“…The private sector and science, particularly, are starting to use big data in their everyday activities, for example, for business intelligence (Chen et al 2012). Retailers like Walmart (Bryant et al 2008), Sears (Henschen 2012) and Amazon (Kelly 2013) use big data to better understand their customers and their buying decisions.…”

Section: Introductionmentioning

confidence: 99%

Big data in the public sector: Uncertainties and readiness

et al. 2016

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Big data is being implemented with success in the private sector and science. Yet the public sector seems to be falling behind, despite the potential value of big data for government. Government organizations do recognize the opportunities of big data but seem uncertain about whether they are ready for the introduction of big data, and if they are adequately equipped to use big data. This paper addresses those uncertainties. It presents an assessment framework for evaluating public organizations' big data readiness. Doing so demystifies the concept of big data, as it is expressed in terms of specific and measureable organizational characteristics. The framework was tested by applying it to organizations in the Dutch public sector. The results suggest that organizations may be technically capable of using big data, but they will not significantly gain from these activities if the applications do not fit their organizations and main statutory tasks. The framework proved helpful in pointing out areas where public sector organizations could improve, providing guidance on how government can become more big data ready in the future.

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Section: Introductionmentioning

confidence: 99%

Big data in the public sector: Uncertainties and readiness

et al. 2016

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“…Also relevant according to the literature are novel Big Data benchmarks for benchmarking relational databases (e.g., BigBench [19] and OLTP [14]). However, although they come with scalable data and task generators, these benchmarks are solely focused on the benchmarking of relational databases and are not benchmarking frameworks.…”

Section: Related Workmentioning

confidence: 99%

“…The resource monitoring component uses Prometheus 19 to collect information about the hardware resources used by the benchmarked system. The benchmark can request this information to include it into its evaluation.…”

Section: Resource Monitoringmentioning

confidence: 99%

HOBBIT: A platform for benchmarking Big Linked Data

Röder

Kuchelev

Ngomo

2020

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Editor: Paul Groth (https://orcid.org/0000-0003-0183-6910) Solicited reviews: Victor de Boer (https://orcid.org/0000-0001-9079-039X); Laura Rettig (https://orcid.org/0000-0002-9765-0549)Abstract. An increasing number of solutions aim to support the steady increase of the number of requirements and requests for Linked Data at scale. This plethora of solutions leads to a growing need for objective means that facilitate the selection of adequate solutions for particular use cases. We hence present HOBBIT, a distributed benchmarking platform designed for the unified execution of benchmarks for Linked Data solutions. The HOBBIT benchmarking platform is based on the FAIR principles and is the first benchmarking platform able to scale up to benchmarking real-world scenarios for Big Linked Data solutions. Our online instance of the platform has more than 300 registered users and offers more than 40 benchmarks. It has been used in eleven benchmarking challenges and for more than 13000 experiments. We give an overview of the results achieved during 2 of these challenges and point to some of the novel insights that were gained from the results of the platform. HOBBIT is open-source and available at http://github.com/hobbit-project.This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution License (CC BY 4.0). 2451-8484 © 2020 -IOS Press and the authors. 16 M. Röder et al. / HOBBIT

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“…HiBench suite contains sets of synthetic and real-world workloads to test the performance of applications over Hadoop. Other benchmark suites, such as BigBench [29], were also considered. However, the overhead of running Hive queries to prepare the data for the workloads is very large in BigBench, making the benchmark process more difficult on our 4-node cluster.…”

Section: A Apparatusmentioning

confidence: 99%

Power Efficient MapReduce Workload Acceleration Using Integrated-GPU

Kim

Bottleson

Jin

et al. 2015

2015 IEEE First International Conference on Big Data Computing Service and Applications

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With the pervasiveness of MapReduce -one of the most prominent programming models for data parallelism in Apache Hadoop -, many researchers and developers have spent tremendous effort attempting to boost the computational speed and energy efficiency of MapReduce-based big data processing. However, the scalable and fault-tolerant nature of MapReduce introduces additional costs in disk IO and data transfer, caused by streaming intermediate outputs to disk. In light of these issues, many interesting research projects have been initiated with the goal of improving the compute speed and power efficiency of compute-intensive cloud computing workloads; several with the addition of discrete GPUs. In this work, we present a modified MapReduce approach focused on the iterative clustering algorithms in the Apache Mahout machine learning library that leverage the acceleration potential of the Intel integrated GPU in a multi-node cluster environment. The accelerated framework shows varying levels of speed-up (≈45x for Map tasks-only, ≈4.37x for the entire K-means clustering) as evaluated using the HiBench benchmark suite. Based on various experiments and in-depth analysis, we find that utilizing the integrated GPU via OpenCL offers significant performance and power efficiency gains over the original CPU based approach. Further analysis is also done to understand the correlations between compute, IO and power efficiency. As such, our results show that embracing the integrated GPU in the Hadoop MapReduce framework represents a promising advance in adding cost and energy efficient compute parallelism to a data parallel multi-node environment.

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BigBench

Cited by 271 publications

References 13 publications

Big data in the public sector: Uncertainties and readiness

Big data in the public sector: Uncertainties and readiness

HOBBIT: A platform for benchmarking Big Linked Data

Power Efficient MapReduce Workload Acceleration Using Integrated-GPU

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