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International Conference on Big Data Analytics and Knowledge Discovery

DaWaK 2022: Big Data Analytics and Knowledge Discovery pp 241–246Cite as

Explainable Recommendations for Wearable Sensor Data

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13428))

Abstract

This paper presents an approach to explore sensor data and learn rules based on the patterns detected in the data. Our approach is a direct modification of the Apriori algorithm with a lookback mechanism that allows us to consider specific temporal windows. The inferred knowledge can be used to provide users with predictions based on historical data as well as personalized, explainable recommendations towards achieving a goal.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Verona, Verona, Italy

    Niccoló Marastoni, Barbara Oliboni & Elisa Quintarelli

Authors
  1. Niccoló Marastoni
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  2. Barbara Oliboni
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  3. Elisa Quintarelli
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Corresponding author

Correspondence to Niccoló Marastoni .

Editor information

Editors and Affiliations

  1. Poznań University of Technology, Poznań, Poland

    Robert Wrembel

  2. Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Johann Gamper

  3. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Kotsis

  4. Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Cite this paper

Marastoni, N., Oliboni, B., Quintarelli, E. (2022). Explainable Recommendations for Wearable Sensor Data. In: Wrembel, R., Gamper, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2022. Lecture Notes in Computer Science, vol 13428. Springer, Cham. https://doi.org/10.1007/978-3-031-12670-3_21

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  • DOI: https://doi.org/10.1007/978-3-031-12670-3_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12669-7

  • Online ISBN: 978-3-031-12670-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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