Earticle

초록

영어

Big Data in the form of streaming, the data stream mining, has received a great deal of attention for some time. With disparate multiple sensors, sensors are able to gentrify the information they want to acquire. In this paper, the ways to encode a wide range of sensor data that is continuously reported is proposed. These encoding methods enable higher level analysis than identify the frequent pattern or association rules. It is essential that sensors are distributed and extract various and detailed information about the context that was sensed. This study suggests that it is essential that the sensor data is encoded in a reasonable and valid way of context inference and extracting a variety of quality information even in the data stream environment for on-off analysis of the large amount of sensor data that continuously flow in through the sensor data encoding method.

목차

Abstract
 1. Introduction
 2. Related Research
 3. Continuous Signal Preprocessing for Context Inference
 4. An Experiment and Evaluation
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Younghwan Oh [ Department of Information and Communication, Korea Nazarene University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605