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Next-Gen Digital Services. A Retrospective and Roadmap for Service Computing of the Future pp 236–248Cite as

About the Quality of Data and Services in Natural Sciences

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 12521)

Abstract

Managing data related to natural sciences poses new and challenging problems as it is impossible to represent reality on a one-to-one scale, and imprecision has to be taken into account, both in data memorization and in its processing. Machine learning has been a key enabler in the context of information extraction from natural sciences data. However, data-driven results are strongly affected by the volume, the sparsity and different types of imprecision in the available sources. Therefore, it becomes pivotal to associate both to data and to data-driven services information about their quality, in order to effectively interpret the results. Different levels of granularity and multiple data modalities captured from the same processes could coexist, due to technological constraints or other intrinsic limiting factors. In addition, different levels of granularity might be also the result of application requirements, and outcomes at multiple levels of precision needs to be provided. Affinities of quality issues in domains such as chemistry, biology, and geoinformatics are discussed in the paper.

Keywords

  • Data quality
  • Quality of service
  • Machine learning

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Notes

  1. 1.

    http://www.crowd4sdg.eu/.

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Acknowledgements

This work was funded by the European Commission H2020 project Crowd4SDG “Citizen Science for Monitoring Climate Impacts and Achieving Climate Resilience” under project No. 872944. This work expresses the opinions of the authors and not necessarily those of the European Commission. The European Commission is not liable for any use that may be made of the information contained in this work.

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Authors and Affiliations

  1. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy

    Barbara Pernici, Francesca Ratti & Gabriele Scalia

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  1. Barbara Pernici
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Correspondence to Barbara Pernici .

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Editors and Affiliations

  1. University of Stuttgart, Stuttgart, Germany

    Prof. Dr. Marco Aiello

  2. The University of Sydney, Camperdown, NSW, Australia

    Athman Bouguettaya

  3. Eindhoven University of Technology, ’s Hertogenbosch, The Netherlands

    Damian Andrew Tamburri

  4. Tilburg University, ’s Hertogenbosch, The Netherlands

    Prof. Willem-Jan van den Heuvel

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Pernici, B., Ratti, F., Scalia, G. (2021). About the Quality of Data and Services in Natural Sciences. In: Aiello, M., Bouguettaya, A., Tamburri, D.A., van den Heuvel, WJ. (eds) Next-Gen Digital Services. A Retrospective and Roadmap for Service Computing of the Future. Lecture Notes in Computer Science(), vol 12521. Springer, Cham. https://doi.org/10.1007/978-3-030-73203-5_18

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  • DOI: https://doi.org/10.1007/978-3-030-73203-5_18

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