Browsing by Author "Yu, Ching-Tzu"

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Automatic Clustering and Semantic Annotation for Dynamic IoT Sensor Data
(2018-01-01)
Yu, Ching-Tzu
;
Zou, Yu-Hui
;
Li, Hao-Yu
;
Lin, Szu-Yin
;
資訊管理與財務金融系註：原資管所+財金所
In a dynamic IoT environment, distributed sensors are used to collect real-time data continually. However, it is difficult to transform the dynamic data into a machine-readable and machine-interpretable form. we propose a semantic annotation approach to annotate sensor data via semantics. Firstly, this approach builds an ontology based on Semantic Sensor Network Ontology (SSN Ontology) for dynamic IoT sensor data. Then, the new knowledge is collected from input data by using the K-Means clustering, and to update the semantic information into the base ontology. The updated ontology forms the basis for semantic annotation.
Dynamic Data Driven-based Automatic Clustering and Semantic Annotation for Internet of Things Sensor Data
(2019-01-01)
Lin, Szu-Yin
;
Li, Jun-Bin
;
Yu, Ching-Tzu
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Faced 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.