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PolyBench: The First Benchmark for Polystores

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Performance Evaluation and Benchmarking for the Era of Artificial Intelligence (TPCTC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11135))

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Abstract

Modern business intelligence requires data processing not only across a huge variety of domains but also across different paradigms, such as relational, stream, and graph models. This variety is a challenge for existing systems that typically only support a single or few different data models. Polystores were proposed as a solution for this challenge and received wide attention both in academia and in industry. These are systems that integrate different specialized data processing engines to enable fast processing of a large variety of data models. Yet, there is no standard to assess the performance of polystores. The goal of this work is to develop the first benchmark for polystores. To capture the flexibility of polystores, we focus on high level features in order to enable an execution of our benchmark suite on a large set of polystore solutions.

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Notes

  1. 1.

    We partially benefitted from the library https://github.com/codemaniac/sopex.

  2. 2.

    https://www.schufa.de.

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Acknowledgments

This work has been supported by the European Commission through Proteus (ref. 687691) and Streamline (ref. 688191) and by the German Ministry for Education and Research as Berlin Big Data Center BBDC (funding mark 01IS14013A).

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Authors and Affiliations

  1. DFKI, Kaiserslautern, Germany

    Jeyhun Karimov, Tilmann Rabl & Volker Markl

  2. TU Berlin, Berlin, Germany

    Tilmann Rabl & Volker Markl

Authors
  1. Jeyhun Karimov
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  2. Tilmann Rabl
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  3. Volker Markl
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Corresponding author

Correspondence to Jeyhun Karimov .

Editor information

Editors and Affiliations

  1. Advanced Micro Systems, Inc., Santa Clara, CA, USA

    Raghunath Nambiar

  2. Oracle Corporation, Redwood Shores, CA, USA

    Meikel Poess

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Karimov, J., Rabl, T., Markl, V. (2019). PolyBench: The First Benchmark for Polystores. In: Nambiar, R., Poess, M. (eds) Performance Evaluation and Benchmarking for the Era of Artificial Intelligence. TPCTC 2018. Lecture Notes in Computer Science(), vol 11135. Springer, Cham. https://doi.org/10.1007/978-3-030-11404-6_3

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  • DOI: https://doi.org/10.1007/978-3-030-11404-6_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-11403-9

  • Online ISBN: 978-3-030-11404-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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