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Simulation-Based Data Acquisition

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Principles of Data Science

Abstract

In data science, the application of most approaches requires the existence of big data from a real-world system. Due to access limitations, nonexistence of the system, or temporal as well as economic restrictions, such data might not be accessible or available. To overcome a lack of real-world data, this chapter introduces simulation-based data acquisition as method for the generation of artificial data that serves as a substitute when applying data science techniques. Instead of gathering data from the real-world system, computer simulation is used to model and execute artificial systems that can provide a more accessible, economic, and robust source of big data. To this end, it is outlined how data science can benefit from simulation and vice versa. Specific approaches are introduced for the design and execution of experiments, and a selection of simulation frameworks is presented that facilitates the conducting of simulation studies for novice and professional users.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Media Technology, Internet of Things and People Research Center (IoTaP), Malmö University, Malmö, Sweden

    Fabian Lorig

  2. Center for Informatics Research and Technology, Trier University, Trier, Germany

    Ingo J. Timm

Authors
  1. Fabian Lorig
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  2. Ingo J. Timm
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Corresponding author

Correspondence to Fabian Lorig .

Editor information

Editors and Affiliations

  1. University of Georgia, Athens, GA, USA

    Hamid R. Arabnia

  2. University of Detroit Mercy, Detroit, MI, USA

    Kevin Daimi

  3. University of Hamburg, Hamburg, Hamburg, Germany

    Robert Stahlbock

  4. Features Analytics, Nivelles, Belgium

    Cristina Soviany

  5. University of Hamburg, Hamburg, Hamburg, Germany

    Leonard Heilig

  6. University of Hamburg, Hamburg, Hamburg, Germany

    Kai Brüssau

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Lorig, F., Timm, I.J. (2020). Simulation-Based Data Acquisition. In: Arabnia, H.R., Daimi, K., Stahlbock, R., Soviany, C., Heilig, L., Brüssau, K. (eds) Principles of Data Science. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-43981-1_1

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  • DOI: https://doi.org/10.1007/978-3-030-43981-1_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-43980-4

  • Online ISBN: 978-3-030-43981-1

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