Semantic Data Mining in Ubiquitous Sensing: A Survey
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 t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/13/4322 |
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| author | Grzegorz J. Nalepa Szymon Bobek Krzysztof Kutt Martin Atzmueller |
| author_facet | Grzegorz J. Nalepa Szymon Bobek Krzysztof Kutt Martin Atzmueller |
| author_sort | Grzegorz J. Nalepa |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T10:06:02Z |
| format | Article |
| id | doaj.art-eb3e34cd7f424fe99ea7f089c5d0970f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T10:06:02Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-eb3e34cd7f424fe99ea7f089c5d0970f2023-11-22T01:34:29ZengMDPI AGSensors1424-82202021-06-012113432210.3390/s21134322Semantic Data Mining in Ubiquitous Sensing: A SurveyGrzegorz J. Nalepa0Szymon Bobek1Krzysztof Kutt2Martin Atzmueller3Institute of Applied Computer Science and Jagiellonian Human-Centered Artificial Intelligence Laboratory (JAHCAI), ul. Prof. Stanislawa Lojasiewicza 11, Jagiellonian University, 30-348 Krakow, PolandInstitute of Applied Computer Science and Jagiellonian Human-Centered Artificial Intelligence Laboratory (JAHCAI), ul. Prof. Stanislawa Lojasiewicza 11, Jagiellonian University, 30-348 Krakow, PolandInstitute of Applied Computer Science and Jagiellonian Human-Centered Artificial Intelligence Laboratory (JAHCAI), ul. Prof. Stanislawa Lojasiewicza 11, Jagiellonian University, 30-348 Krakow, PolandSemantic Information Systems Group, Osnabrück University, 49074 Osnabrück, GermanyMining 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.https://www.mdpi.com/1424-8220/21/13/4322semanticsdata miningdeclarative methodsexplainabilityindustrial sensors |
| spellingShingle | Grzegorz J. Nalepa Szymon Bobek Krzysztof Kutt Martin Atzmueller Semantic Data Mining in Ubiquitous Sensing: A Survey Sensors semantics data mining declarative methods explainability industrial sensors |
| title | Semantic Data Mining in Ubiquitous Sensing: A Survey |
| title_full | Semantic Data Mining in Ubiquitous Sensing: A Survey |
| title_fullStr | Semantic Data Mining in Ubiquitous Sensing: A Survey |
| title_full_unstemmed | Semantic Data Mining in Ubiquitous Sensing: A Survey |
| title_short | Semantic Data Mining in Ubiquitous Sensing: A Survey |
| title_sort | semantic data mining in ubiquitous sensing a survey |
| topic | semantics data mining declarative methods explainability industrial sensors |
| url | https://www.mdpi.com/1424-8220/21/13/4322 |
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