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Data-Centric Benchmarking

Data-Centric Benchmarking

Jérôme Darmont
Copyright: © 2018 |Pages: 11
ISBN13: 9781522522553|ISBN10: 1522522557|EISBN13: 9781522522560
DOI: 10.4018/978-1-5225-2255-3.ch154
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MLA

Darmont, Jérôme. "Data-Centric Benchmarking." Encyclopedia of Information Science and Technology, Fourth Edition, edited by Mehdi Khosrow-Pour, D.B.A., IGI Global, 2018, pp. 1772-1782. https://doi.org/10.4018/978-1-5225-2255-3.ch154

APA

Darmont, J. (2018). Data-Centric Benchmarking. In M. Khosrow-Pour, D.B.A. (Ed.), Encyclopedia of Information Science and Technology, Fourth Edition (pp. 1772-1782). IGI Global. https://doi.org/10.4018/978-1-5225-2255-3.ch154

Chicago

Darmont, Jérôme. "Data-Centric Benchmarking." In Encyclopedia of Information Science and Technology, Fourth Edition, edited by Mehdi Khosrow-Pour, D.B.A., 1772-1782. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2255-3.ch154

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Abstract

In data management, both system designers and users casually resort to performance evaluation. Performance evaluation by experimentation on a real system is generally referred to as benchmarking. The aim of this chapter is to present an overview of the major past and present state-of-the-art data-centric benchmarks. This review includes the TPC standard benchmarks, but also alternative or more specialized benchmarks. Surveyed benchmarks are categorized into three families: transaction benchmarks aimed at On-Line Transaction Processing (OLTP), decision-support benchmarks aimed at On-Line Analysis Processing (OLAP) and big data benchmarks. Issues, tradeoffs and future trends in data-centric benchmarking are also discussed.

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