Stream Data Analysis of the Weather on the Location using Principal Component Analysis

주성분 분석을 이용한 지역기반의 날씨의 스트림 데이터 분석

  • Kim, Sang-Yeob (New & Renewable Energy Research Group, Korea Institute of Energy Research) ;
  • Kim, Kwang-Deuk (New & Renewable Energy Research Group, Korea Institute of Energy Research) ;
  • Bae, Kyoung-Ho (Research Institute of Geoinformatics, Korea Association of Surveying & Mapping) ;
  • Ryu, Keun-Ho (Division of Computer Engineering, Chungbuk National University)
  • Received : 2010.02.24
  • Accepted : 2010.04.02
  • Published : 2010.04.30

Abstract

The recent advance of sensor networks and ubiquitous techniques allow collecting and analyzing of the data which overcome the limitation imposed by time and space in real-time for making decisions. Also, analysis and prediction of collected data can support useful and necessary information to users. The collected data in sensor networks environment is the stream data which has continuous, unlimited and sequential properties. Because of the continuous, unlimited and large volume properties of stream data, managing stream data is difficult. And the stream data needs dynamic processing method because of the memory constraint and access limitation. Accordingly, we analyze correlation stream data using principal component analysis. And using result of analysis, it helps users for making decisions.

Keywords

References

  1. Kim, S. H. (2007), Stream Data Prediction and Future Classification using Incremental Model Update Technique. Chungbuk National University master's thesis.
  2. Kim, S. Y., Kim H., Kim, K. D. and Ryu, K. H. (2009), Multi Regression Analysis for Time-series Data in Stream Environment, IWAC 2009. pp. 654-66.
  3. Lee, Y. K., Jung, Y. J. and Ryu, K. H. (2007), Design and Implementation of a System for Environmental Monitoring Sensor Network, In Proceedings of the Conference on APWeb/WAIM Workshop on DBMAN, pp. 223-228.
  4. Manjeshwar, A. and Agrawal, D. P. (2001), TEEN: A routing protocol for enhanced efficiency in wireless sensor networks, International Workshop Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing, pp. 2009-2015.
  5. Ryu, K. H., Lee, S. H., Kim, H. S., Kim, S. Y., Park, S. K. and Jo, Y. B. (2009), The Technology of Real Time Monitering and Processingjhr managing New and Renewable Energy Resource, Research Report, Korea Institute of Energy Research.