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FoodBroker - Generating Synthetic Datasets for Graph-Based Business Analytics

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

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Abstract

We present FoodBroker, a new data generator for benchmarking graph-based business intelligence systems and approaches. It covers two realistic business processes and their involved master and transactional data objects. The interactions are correlated in controlled ways to enable non-uniform distributions for data and relationships. For benchmarking data integration, the generated data is stored in two interrelated databases. The dataset can be arbitrarily scaled and allows comprehensive graph- and pattern-based analysis.

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Notes

  1. 1.

    https://github.com/dbs-leipzig/foodbroker.

  2. 2.

    http://www.biiig.org.

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Acknowledgments

This work is partly funded within the EU program Europa fördert Sachsen of the European Social Fund.

Author information

Authors and Affiliations

  1. University of Leipzig, Leipzig, Germany

    André Petermann, Martin Junghanns & Erhard Rahm

  2. Leipzig University of Applied Sciences, Leipzig, Germany

    André Petermann & Robert Müller

Authors
  1. André Petermann
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  2. Martin Junghanns
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  3. Robert Müller
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  4. Erhard Rahm
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Corresponding author

Correspondence to André Petermann .

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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Petermann, A., Junghanns, M., Müller, R., Rahm, E. (2015). FoodBroker - Generating Synthetic Datasets for Graph-Based Business Analytics. 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_13

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  • DOI: https://doi.org/10.1007/978-3-319-20233-4_13

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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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