스마트홈 지능형 서비스 플랫폼을 위한 데이터 마이닝 기법에 대한 적합도 평가
An Evaluation of the Suitability of Data Mining Algorithms for Smart-Home Intelligent-Service Platforms
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초록
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.
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.
참고문헌 (17)
- Cook, D. J., "CASAS : A smart home in a box", Computer, 46(7), pp.62~69 (2013)
- Cook, D.J., "MavHome : An agent-based smart home", Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, pp.521~524 (2003)
- Deleawe, S., "Predicting air quality in smart environments", Journal of Ambient Intelligence and Smart Environments, 2(2), pp.145~154 (2010)
- Han, D.-M., "Design and implementation of smart home energy management systems based on zigbee", IEEE Transactions on Consumer Electronics, 56(3), pp.1417~1425 (2010)
- Hastie, T., "The elements of statistical learning" (2009), Springer
- Helal, S., "The gator tech smart house : A programmable pervasive space", Computer, 38(3), pp.50~60 (2005)
- Jakkula, V.R., "Detecting anomalous sensor events in smart home data for enhancing the living experience", Proceedings of the 7th AAAI Conference on Artificial Intelligence and Smarter Living, pp.33~37 (2011)
- 정훈, "기계적 모터 고장진단을 위한 머신러닝 기법", 산업경영시스템학회지, 40(1), pp.57~64 (2017), 한국산업경영시스템학회
- Kim, Y. K., "Six elements of Smart-home ecosystem" (2014), Digieco
- 박승환, "Support Vector Machine-Regression을 이용한 주기신호의 이상탐지", 품질경영학회지, 38(3), pp.354~362 (2010), 한국품질경영학회
- Saaty, T. L., "A scaling method for priorities in hierarchical structures", Journal of Mathematical Psychology, 15(3), pp.234~281 (1977)
- Saaty, T, "The Analytic Hierarchy Process : Planning, Priority Setting, Resource Allocation, Advanced book program" (1980), McGraw-Hill
- Spencer, B., "Temperature Forecasts with Stable Accuracy in a Smart Home", Procedia Computer Science, 83, pp.726~733 (2016)
- Suryadevara, N. K., "Forecasting the behavior of an elderly using wireless sensors data in a smart home", Engineering Applications of Artificial Intelligence, 26(10), pp.2641~2652 (2013)
- Tsai, C. -W., "Data mining for internet of things : A survey, Communications Surveys Tutorials", IEEE, 16(1), pp.77~97 (2014)
- Witten, I. H., "Data Mining :Practical machine learning tools and techniques" (2011), Morgan Kaufmann
- Zhou, B., "Smart home energy management systems : Concept, configurations, and scheduling strategies", Renewable and Sustainable Energy Reviews, 61, pp.30~40 (2016)