期刊名称：International Journal of Software Engineering and Its Applications
印刷版ISSN：1738-9984
出版年度：2014
卷号：8
期号：12
页码：65-74
DOI：10.14257/ijseia.2014.8.12.06
出版社：SERSC
摘要：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.
关键词：Context inference; Big Data; Data Fusion; Data Stream
