Senska – Towards an Enterprise Streaming Benchmark

  • Guenter Hesse
  • Benjamin Reissaus
  • Christoph Matthies
  • Martin Lorenz
  • Milena Kraus
  • Matthias Uflacker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10661)


In the light of growing data volumes and continuing digitization in fields such as Industry 4.0 or Internet of Things, data stream processing have gained popularity and importance. Especially enterprises can benefit from this development by augmenting their vital, core business data with up-to-date streaming information. Enriching this transactional data with detailed information from high-frequency data streams allows answering new analytical questions as well as improving current analyses, e.g., regarding predictive maintenance. Comparing such data stream processing architectures for use in an enterprise context, i.e., when combining streaming and business data, is currently a challenging task as there is no suitable benchmark.

In this paper, we give an overview about performance benchmarks in the area of data stream processing. We highlight shortcomings of existing benchmarks and present the need for a new benchmark with a focus on an enterprise context. Furthermore, the ideas behind Senska, a new enterprise streaming benchmark that shall fill this gap, and its architecture are introduced.


Benchmarking Benchmark development Data stream processing Stream processing Internet of Things 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Guenter Hesse
    • 1
  • Benjamin Reissaus
    • 1
  • Christoph Matthies
    • 1
  • Martin Lorenz
    • 1
  • Milena Kraus
    • 1
  • Matthias Uflacker
    • 1
  1. 1.Hasso Plattner InstituteUniversity of PotsdamPotsdamGermany

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