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Scientific Article details

Title Semantic Data Mining in Ubiquitous Sensing: A Survey
ID_Doc 13465
Authors Nalepa, GJ; Bobek, S; Kutt, K; Atzmueller, M
Title Semantic Data Mining in Ubiquitous Sensing: A Survey
Year 2021
Published Sensors, 21, 13
DOI 10.3390/s21134322
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.
Author Keywords semantics; data mining; declarative methods; explainability; industrial sensors
Index Keywords Index Keywords
Document Type Other
Open Access Open Access
Source Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
EID WOS:000671084900001
WoS Category Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation
Research Area Chemistry; Engineering; Instruments & Instrumentation
PDF https://www.mdpi.com/1424-8220/21/13/4322/pdf
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