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Implementation of Descriptive Similarity for Decision Making in Smart Cities

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HCI International 2020 – Late Breaking Papers: Interaction, Knowledge and Social Media (HCII 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12427))

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Abstract

The paper deals with forming the descriptive similarity based on algorithm and a computer program for the decision making support in order to select the suitable solution for implementation from portfolio of the existing experiences related to public transport from various cities, especially in Smart Cities. It helps to satisfy needs in six fields of Smart City and to form rapid decisions for the problems solving. The deeper focus of the work is to develop tools for supporting a decision-making process in which computer systems and people inevitability to participate together. People will not be able to process and analyze the required amounts of data within the required time. However, computers cannot, in principle, decide for humans what to consider as equivalent, what is appropriate and inappropriate for humans. This work focuses on those aspects, which are related to the numerical evaluation of similarity that are needed to make decisions based on analogy, higher prevision of descriptions.

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Acknowledgments

The author of the given work expresses profound to professor Peeter Lorents for assistance in a writing of given clause and to professor Yaroslav Pasternak, Roman Suiatinov for the help with improving the flowchart for writing an RStudio-based software.

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Authors and Affiliations

  1. Estonian Business School, A. Lauteri, 3, Tallinn, Estonia

    Maryna Averkyna

  2. The National University of Ostroh Academy, Seminarska, 2, Ostroh, Ukraine

    Maryna Averkyna

Authors
  1. Maryna Averkyna
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Corresponding author

Correspondence to Maryna Averkyna .

Editor information

Editors and Affiliations

  1. University of Crete and Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

  2. University of Central Florida, Orlando, FL, USA

    Gavriel Salvendy

  3. University of West Florida, Pensacola, FL, USA

    June Wei

  4. Tokyo University of Science, Tokyo, Japan

    Sakae Yamamoto

  5. Tokyo City University, Tokyo, Japan

    Hirohiko Mori

  6. Towson University, Towson, MD, USA

    Gabriele Meiselwitz

  7. Missouri University of Science and Technology, Rolla, MO, USA

    Fiona Fui-Hoon Nah

  8. Missouri University of Science and Technology, Rolla, MO, USA

    Keng Siau

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Averkyna, M. (2020). Implementation of Descriptive Similarity for Decision Making in Smart Cities. In: Stephanidis, C., et al. HCI International 2020 – Late Breaking Papers: Interaction, Knowledge and Social Media. HCII 2020. Lecture Notes in Computer Science(), vol 12427. Springer, Cham. https://doi.org/10.1007/978-3-030-60152-2_2

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  • DOI: https://doi.org/10.1007/978-3-030-60152-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60151-5

  • Online ISBN: 978-3-030-60152-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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