DC Field | Value | Language |
dc.contributor.author | Nalepa, Grzegorz J. | |
dc.contributor.author | Bobek, Szymon | |
dc.contributor.author | Kutt, Krzysztof | |
dc.contributor.author | Atzmueller, Martin | |
dc.date.accessioned | 2021-12-23T16:14:43Z | - |
dc.date.available | 2021-12-23T16:14:43Z | - |
dc.date.issued | 2021 | |
dc.identifier.uri | https://osnascholar.ub.uni-osnabrueck.de/handle/unios/11211 | - |
dc.description.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. | |
dc.description.sponsorship | National Science Centre, PolandNational Science Centre, Poland [NCN 2018/27/Z/ST6/03392]; Interreg North-West Europe program (Interreg NWE), project Di-Plast - Digital Circular Economy for the Plastics Industry [NWE729]; This paper was funded by the National Science Centre, Poland under CHIST-ERA programme, the CHIST-ERA 2017 BDSI PACMEL Project, NCN 2018/27/Z/ST6/03392. Furthermore, the research leading to this article has been funded by the Interreg North-West Europe program (Interreg NWE), project Di-Plast - Digital Circular Economy for the Plastics Industry (NWE729). | |
dc.language.iso | en | |
dc.publisher | MDPI | |
dc.relation.ispartof | SENSORS | |
dc.subject | ARCHITECTURE | |
dc.subject | BIG DATA | |
dc.subject | Chemistry | |
dc.subject | Chemistry, Analytical | |
dc.subject | CONTEXT | |
dc.subject | data mining | |
dc.subject | declarative methods | |
dc.subject | Engineering | |
dc.subject | Engineering, Electrical & Electronic | |
dc.subject | explainability | |
dc.subject | GENERATION | |
dc.subject | GRAPHS | |
dc.subject | industrial sensors | |
dc.subject | Instruments & Instrumentation | |
dc.subject | KNOWLEDGE DISCOVERY | |
dc.subject | ONTOLOGY | |
dc.subject | semantics | |
dc.subject | TECHNOLOGIES | |
dc.subject | THINGS | |
dc.subject | WEB | |
dc.title | Semantic Data Mining in Ubiquitous Sensing: A Survey | |
dc.type | review | |
dc.identifier.doi | 10.3390/s21134322 | |
dc.identifier.isi | ISI:000671084900001 | |
dc.description.volume | 21 | |
dc.description.issue | 13 | |
dc.contributor.orcid | 0000-0001-5453-9763 | |
dc.contributor.orcid | 0000-0002-8182-4225 | |
dc.contributor.orcid | 0000-0002-6350-8405 | |
dc.contributor.researcherid | A-2501-2017 | |
dc.contributor.researcherid | ABE-8339-2021 | |
dc.contributor.researcherid | B-3308-2013 | |
dc.identifier.eissn | 14248220 | |
dc.publisher.place | ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND | |
dcterms.isPartOf.abbreviation | Sensors | |
dcterms.oaStatus | Green Published, gold | |
crisitem.author.dept | FB 06 - Mathematik/Informatik/Physik | - |
crisitem.author.deptid | fb6 | - |
crisitem.author.parentorg | Universität Osnabrück | - |
crisitem.author.netid | AtMa176 | - |