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Review
. 2021 Jun 24;21(13):4322.
doi: 10.3390/s21134322.

Semantic Data Mining in Ubiquitous Sensing: A Survey

Grzegorz J Nalepa  1   2 , Szymon Bobek  1   2 , Krzysztof Kutt  1 , Martin Atzmueller  3
Affiliations
Review

Semantic Data Mining in Ubiquitous Sensing: A Survey

Grzegorz J Nalepa et al. Sensors (Basel). .

Abstract

Mining ubiquitous sensing data is important but also challenging, due to many factors, such as heterogeneous large-scale data that is often at various levels of abstraction. This also relates particularly to the important aspects of the explainability and interpretability of the applied models and their results, and thus ultimately to the outcome of the data mining process. With this, in general, the inclusion of domain knowledge leading towards semantic data mining approaches is an emerging and important research direction. This article aims to survey relevant works in these areas, focusing on semantic data mining approaches and methods, but also on selected applications of ubiquitous sensing in some of the most prominent current application areas. Here, we consider in particular: (1) environmental sensing; (2) ubiquitous sensing in industrial applications of artificial intelligence; and (3) social sensing relating to human interactions and the respective individual and collective behaviors. We discuss these in detail and conclude with a summary of this emerging field of research. In addition, we provide an outlook on future directions for semantic data mining in ubiquitous sensing contexts.

Keywords: data mining; declarative methods; explainability; industrial sensors; semantics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Listing 1
Listing 1
An example of a semantized data sample taken from OpenWeatherMap reporting air temperature at Jagiellonian University.
Listing 2
Listing 2
A sample query to extract all temperature measurements for the Kraków, PL area.
Figure 1
Figure 1
Observation and related concepts in SOSA ontology. Reprinted from [88], with permission from Elsevier.
Figure 2
Figure 2
Percentage of advanced technology uptake in Industry 4.0 and AI in this uptake. Generated with https://ati.ec.europa.eu/data-dashboard (accessed on 8 April 2021).
Figure 3
Figure 3
Trend in percentage uptake of advanced technology in 2019 and 2020. Generated with https://ati.ec.europa.eu/data-dashboard (accessed on 8 April 2021).
Figure 4
Figure 4
Three levels of the Cyber–Physical system in Industry 4.0 [110].
Figure 5
Figure 5
Knowledge source, its formalization and application to different ML/DM stages [8].
See this image and copyright information in PMC

References

    1. Cuzzocrea A., Song I.Y., Davis K.C. Analytics Over Large-Scale Multidimensional Data: The Big Data Revolution!; Proceedings of the 14th International Workshop on Data Warehousing and OLAP at 20th International Conference on Information and Knowledge Management, CIKM 2011; Glasgow, UK. 24–28 October 2011; New York, NY, USA: ACM; 2011. pp. 101–104. - DOI
    1. Klein D., Tran-Gia P., Hartmann M. Big Data. Inform. Spektrum. 2013;36:319–323. doi: 10.1007/s00287-013-0702-3. - DOI
    1. Luengo J., García-Gil D., Ramírez-Gallego S., García S., Herrera F. Big Data Preprocessing. Springer; Berlin/Heidelberg, Germany: 2020. Smart Data; pp. 45–51.
    1. Molnar C. Interpretable Machine Learning. [(accessed on 28 February 2020)]; Available online: https://christophm.github.io/interpretable-ml-book/
    1. Dou D., Wang H., Liu H. Semantic Data mining: A Survey of Ontology-Based Approaches; Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015); Anaheim, CA, USA. 7–9 February 2015; pp. 244–251.

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