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A machine learning approach for predictive maintenance for mobile phones service providers

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Advances on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC 2016)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 1))

Abstract

The problem of predictive maintenance is a very crucial one for every technological company. This is particularly true for mobile phones service providers, as mobile phone networks require continuous monitoring. The ability of previewing malfunctions is crucial to reduce maintenance costs and loss of customers. In this paper we describe a preliminary study in predicting failures in a mobile phones networks based on the analysis of real data. A ridge regression classifier has been adopted as machine learning engine, and interesting and promising conclusion were drawn from the experimental data.

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Author information

Authors and Affiliations

  1. DIETI, Universit`a Di Napoli Federico II, Naples, Italy

    Anna Corazza, Francesco Isgrò, Luca Longobardo & R. Prevete

Authors
  1. Anna Corazza
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  2. Francesco Isgrò
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  3. Luca Longobardo
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  4. R. Prevete
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Corresponding author

Correspondence to Francesco Isgrò .

Editor information

Editors and Affiliations

  1. Campus Nord,Ed. Omega (Room 109), Technical University of Catalonia Campus Nord,Ed. Omega (Room 109), Barcelona, Spain

    Fatos Xhafa

  2. Fukuoka Institute of Technology , Fukuoka, Japan

    Leonard Barolli

  3. Federico II, Università degli Studi di Napoli Federico II, Napoli, Italy

    Flora Amato

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Corazza, A., Isgrò, F., Longobardo, L., Prevete, R. (2017). A machine learning approach for predictive maintenance for mobile phones service providers. In: Xhafa, F., Barolli, L., Amato, F. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2016. Lecture Notes on Data Engineering and Communications Technologies, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-49109-7_69

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  • DOI: https://doi.org/10.1007/978-3-319-49109-7_69

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49108-0

  • Online ISBN: 978-3-319-49109-7

  • eBook Packages: EngineeringEngineering (R0)

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