Next Article in Journal
Real-Time Human Recognition at Night via Integrated Face and Gait Recognition Technologies
Previous Article in Journal
ATARI: A Graph Convolutional Neural Network Approach for Performance Prediction in Next-Generation WLANs
Previous Article in Special Issue
Field-Portable Microplastic Sensing in Aqueous Environments: A Perspective on Emerging Techniques
 
 
Review

Semantic Data Mining in Ubiquitous Sensing: A Survey

1
Institute of Applied Computer Science and Jagiellonian Human-Centered Artificial Intelligence Laboratory (JAHCAI), ul. Prof. Stanislawa Lojasiewicza 11, Jagiellonian University, 30-348 Krakow, Poland
2
Department of Applied Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland
3
Semantic Information Systems Group, Osnabrück University, 49074 Osnabrück, Germany
*
Authors to whom correspondence should be addressed.
Academic Editor: Mehmet Rasit Yuce
Sensors 2021, 21(13), 4322; https://doi.org/10.3390/s21134322
Received: 22 April 2021 / Revised: 15 June 2021 / Accepted: 18 June 2021 / Published: 24 June 2021
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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. View Full-Text
Keywords: semantics; data mining; declarative methods; explainability; industrial sensors semantics; data mining; declarative methods; explainability; industrial sensors
Show Figures

Figure 1

MDPI and ACS Style

Nalepa, G.J.; Bobek, S.; Kutt, K.; Atzmueller, M. Semantic Data Mining in Ubiquitous Sensing: A Survey. Sensors 2021, 21, 4322. https://doi.org/10.3390/s21134322

AMA Style

Nalepa GJ, Bobek S, Kutt K, Atzmueller M. Semantic Data Mining in Ubiquitous Sensing: A Survey. Sensors. 2021; 21(13):4322. https://doi.org/10.3390/s21134322

Chicago/Turabian Style

Nalepa, Grzegorz J., Szymon Bobek, Krzysztof Kutt, and Martin Atzmueller. 2021. "Semantic Data Mining in Ubiquitous Sensing: A Survey" Sensors 21, no. 13: 4322. https://doi.org/10.3390/s21134322

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop