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Benchmarking Elastic Query Processing on Big Data

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Big Data Benchmarking (WBDB 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8991))

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Abstract

Existing analytical query benchmarks, such as TPC-H, often assess database system performance on on-premises hardware installations. On the other hand, some benchmarks for cloud-based analytics deal with flexible infrastructure, but often focus on simpler queries and semi-structured data. With our benchmark draft we attempt to bridge the gap by challenging analytical platforms to answer complex queries on structured business data while leveraging the elastic infrastructure of the cloud to satisfy performance requirements.

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Author information

Authors and Affiliations

  1. Fakultät Für Informatik, Technische Universität München, Bolzmannstraße 3, 85648, Garching, Germany

    Dimitri Vorona, Florian Funke, Alfons Kemper & Thomas Neumann

Authors
  1. Dimitri Vorona
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  2. Florian Funke
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  3. Alfons Kemper
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  4. Thomas Neumann
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Corresponding author

Correspondence to Dimitri Vorona .

Editor information

Editors and Affiliations

  1. University of Toronto, Toronto, Ontario, Canada

    Tilmann Rabl

  2. SAP SE, Köln, Germany

    Kai Sachs

  3. Server Technologies, Oracle Corporation, Redwood Shores, California, USA

    Meikel Poess

  4. University of California at San Diego, La Jolla, CA, USA

    Chaitanya Baru

  5. Middleware Systems Research Group, Toronto, Ontario, Canada

    Hans-Arno Jacobson

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Vorona, D., Funke, F., Kemper, A., Neumann, T. (2015). Benchmarking Elastic Query Processing on Big Data. In: Rabl, T., Sachs, K., Poess, M., Baru, C., Jacobson, HA. (eds) Big Data Benchmarking. WBDB 2014. Lecture Notes in Computer Science(), vol 8991. Springer, Cham. https://doi.org/10.1007/978-3-319-20233-4_5

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  • DOI: https://doi.org/10.1007/978-3-319-20233-4_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20232-7

  • Online ISBN: 978-3-319-20233-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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