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스마트홈 지능형 서비스 플랫폼을 위한 데이터 마이닝 기법에 대한 적합도 평가
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- Issue Date
- 2017-06
- Citation
- 산업경영시스템학회지, v.40, no.2, pp.68-77
- ISSN
- 2005-0461
- Publisher
- 한국산업경영시스템학회
- Language
- Korean
- Type
- Journal Article
- Abstract
- In order to implement the smart home environment, we need an intelligence service platform that learns the user’s life style and behavioral patterns, and recommends appropriate services to the user. The intelligence service platform should embed a couple of effective and efficient data mining algorithms for learning from the data that is gathered from the smart home environment. In this study, we evaluate the suitability of data mining algorithms for smart home intelligent service platforms. In order to do this, we first develop an intelligent service scenario for smart home environment, which is utilized to derive functional and technical requirements for data mining algorithms that is equipped in the smart home intelligent service platform. We then evaluate the suitability of several data mining algorithms by employing the analytic hierarchy process technique. Applying the analytical hierarchy process technique, we first score the importance of functional and technical requirements through a hierarchical structure of pairwise comparisons made by experts, and then assess the suitability of data mining algorithms for each functional and technical requirements. There are several studies for smart home service and platforms, but most of the study have focused on a certain smart home service or a certain service platform implementation. In this study, we focus on the general requirements and suitability of data mining algorithms themselves that are equipped in smart home intelligent service platform. As a result, we provide a general guideline to choose appropriate data mining techniques when building a smart home intelligent service platform.
- KSP Keywords
- Analytical Hierarchy process, Data mining(DM), Process technique, Smart Homes(SH), Smart home environment, Technical requirement, analytic hierarchy process, behavioral patterns, data mining algorithms, data mining techniques, hierarchical structure