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Big Data Science

KI - Künstliche Intelligenz volume 32, pages 27–36 (2018)Cite this article

Abstract

In ever more disciplines, science is driven by data, which leads to data analytics becoming a primary skill for researchers. This includes the complete process from data acquisition at sensors, over pre-processing and feature extraction to the use and application of machine learning. Sensors here often produce a plethora of data that needs to be dealt with in near-realtime, which requires a combined effort of implementations at the hardware level to high-level design of data flows. In this paper we outline two use-cases of this wide span of data analysis for science in a real-world example in astroparticle physics. We outline a high-level design approach which is capable of defining the complete data flow from sensor hardware to final analysis.

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Notes

  1. The term has been used in Silicon Graphics by John Massey since 1998, but started to be popular only in 2012 with a trending peak in 2016 according to Google trends.

  2. Project C3 by Wolfgang Rhode, Katharina Morik, Tim Ruhe investigates astrophysical data from the IceCube project and Cherenkov telescopes. Project C5 by Bernhard Spaan and Jens Teubner discusses the data of the LHCb experiment at the Large Hadron Collider (LHC) facility in Geneva.

  3. The TU Dortmund university offers studies in data science within the statistics faculty since 2002. Within the computer science faculty, students may specialize on data science.

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Acknowledgements

This work has been supported by the DFG, Collaborative Research Center SFB 876 (http://sfb876.tu-dortmund.de/), projects C3 and A1.

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Authors and Affiliations

  1. TU Dortmund, Otto-Hahn-Strasse, 1244227, Dortmund, Germany

    Katharina Morik, Christian Bockermann & Sebastian Buschjäger

Authors
  1. Katharina Morik
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  2. Christian Bockermann
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  3. Sebastian Buschjäger
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Correspondence to Christian Bockermann.

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Morik, K., Bockermann, C. & Buschjäger, S. Big Data Science. Künstl Intell 32, 27–36 (2018). https://doi.org/10.1007/s13218-017-0522-8

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Keywords

  • Big data
  • Data science