• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.675-682
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• 2021

In an environment in which a limited type and number of sensors are used, a demand for acquiring various context information may appear. In this study, a new method for acquiring various context information than before was proposed in an environment in which a limited number of sensors are required. To this end, a clue was obtained from the Exon-Intron theory, which is gaining great interest in the field of biology, and a method for acquiring various context information was proposed based on this. By applying Exon-Intron's selective cutting and combining method, events of each sensor were efficiently cut and each event data was combined and utilized, thereby realizing the diversification of the acquired context information.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.799-806
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• 2021

In the IoT environment, event data from sensors is continuously reported over time. Event data obtained in this trend is accumulated indefinitely, so a method for efficient analysis and management of data is required. In this study, a data stream segmentation method was proposed to support the effective selection and utilization of event data from sensors that are continuously reported and received. An identifier for identifying the point at which to start the analysis process was selected. By introducing the role of these identifiers, it is possible to clarify what is being analyzed and to reduce data throughput. The identifier for stream segmentation proposed in this study is a semantic-oriented data stream segmentation method based on the event occurrence of each stream. The existence of identifiers in stream processing can be said to be useful in terms of providing efficiency and reducing its costs in a large-volume continuous data inflow environment.