完整後設資料紀錄
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dc.contributor.authorLin, Szu-Yinen_US
dc.contributor.authorLi, Jun-Binen_US
dc.contributor.authorYu, Ching-Tzuen_US
dc.date.accessioned2019-08-02T02:18:27Z-
dc.date.available2019-08-02T02:18:27Z-
dc.date.issued2019-01-01en_US
dc.identifier.issn0914-4935en_US
dc.identifier.urihttp://dx.doi.org/10.18494/SAM.2019.2333en_US
dc.identifier.urihttp://hdl.handle.net/11536/152291-
dc.description.abstractFaced with the advent of the era of smart Internet of Things (IoT), a large amount of sensor data and a large number of intelligent applications have been introduced into our lives. However, the dynamic and multimodal nature of data makes it challenging to transform them into machine-readable and machine-interpretable forms. In this study, a semantic annotation method is proposed to annotate sensor data through semantics. First, the method constructs an initial ontology based on the semantic sensor network (SSN) ontology for dynamic loT sensor data. Second, through K-means clustering, new knowledge is extracted from input data, and the semantic information is used for updating the initial ontology. The updated ontology then forms the basis of semantic annotation. In this study, an experiment is performed to analyze the data collected from sensors every 10 s for a period of one month. From the results of simulation experiments, we found useful knowledge from new data. With more available knowledge, sensor data can be annotated with higher adequacy.en_US
dc.language.isoen_USen_US
dc.subjectclusteringen_US
dc.subjectsemantic annotationen_US
dc.subjectontologyen_US
dc.subjectInternet of Thingsen_US
dc.subjectsensor dataen_US
dc.titleDynamic Data Driven-based Automatic Clustering and Semantic Annotation for Internet of Things Sensor Dataen_US
dc.typeArticleen_US
dc.identifier.doi10.18494/SAM.2019.2333en_US
dc.identifier.journalSENSORS AND MATERIALSen_US
dc.citation.volume31en_US
dc.citation.issue6en_US
dc.citation.spage1789en_US
dc.citation.epage1801en_US
dc.contributor.department資訊管理與財務金融系 註:原資管所+財金所zh_TW
dc.contributor.departmentDepartment of Information Management and Financeen_US
dc.identifier.wosnumberWOS:000470871500001en_US
dc.citation.woscount0en_US
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