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A Study on the Virtuous Circle Self-Learning Methods for Knowledge Enhancement
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- Authors
- Jaehak Yu, Young-Min Kim, SoonHyun Kwon, Kwihoon Kim, Nae-Soo Kim, Sun-Jin Kim, Cheol-Sig Pyo
- Issue Date
- 2017-02
- Citation
- International Conference on Platform Technology and Service (PlatCon) 2017, pp.46-51
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Recently, along with the development of ICT technology, there is a significant increase in Internet of Everything (IoE) more advanced than Internet of Things (IoT) is one of the technologies that not only connects people and things, data and services, but also provides users with more intelligent and smart services However, it is difficult to efficiently process data generated from various kinds of things and services in this IoT environment. Also, it is difficult to provide objective analysis and knowledge-based services in an adaptive manner in response to IoE environment changes. In this paper, we propose a virtuous circle based knowledge convergence and extension model to accommodate the above intrinsic requirements. The proposed model exploits 1) the advanced information during preprocessing phase got by analyzing the high volume data of various IoE devices on real-time and 2) gives machine learning based learning models and their results during learning model phase to support the decision making accurately. These self-learning and learning results can be generated, converged, inferenced, and expanded with new knowledge of data processing and learning. Finally, our system has the added advantage of providing knowledge and understanding of physical things, virtual things, domains and services, and analysis information so that the user can easily understand them.
- KSP Keywords
- Advanced information, Data processing, High volume, Internet of Everything, Internet of thing(IoT), IoT environment, Knowledge Enhancement, Knowledge convergence, Knowledge-based, Learning methods, Learning model