DocumentCode :
2191881
Title :
Continuous Sensor Data Mining Model and System Design
Author :
Chai, Duck Jin ; Jin, Long ; Bae, Kyoung Ho ; Hwang, Buhyun ; Ryu, Keun Ho
Author_Institution :
CBNUBK21 Chungbuk Inf. Technol. Center, Cheongju
fYear :
2008
fDate :
8-11 July 2008
Firstpage :
501
Lastpage :
506
Abstract :
The sensor data, which is inputted from sensor network, is stream data having continuous and infinite properties. The previous data mining techniques canpsilat directly be used in the sensor data mining because of these properties of sensor data. Also, most of application services in the sensor network are only event alert services which perceive the events from sensors and alert the events to the supervisor. In this paper, we define continuous sensor data mining model and design a system based on the model. The system can service useful knowledge by continuous sensor data mining using gathered data from sensor in the sensor network. First, we classify sensor data to the three data types, which are each simple sensor data, continuous sensor data, and sensor event data, and define sensor data mining models about outlier analysis, pattern analysis, and prediction analysis. After the definition of model, we design a system which can be used in application services like u-Silvercare, Sea Ranching Program, City Environment Management, etc., based on these mining models in sensor network environment.
Keywords :
data mining; sensor fusion; City Environment Management; Sea Ranching Program; continuous sensor data mining model; event alert services; outlier analysis; pattern analysis; prediction analysis; sensor network; u-Silvercare; Continuous Sensor Data Mining; System Design;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer and Information Technology Workshops, 2008. CIT Workshops 2008. IEEE 8th International Conference on
Conference_Location :
Sydney, QLD
Print_ISBN :
978-0-7695-3242-4
Electronic_ISBN :
978-0-7695-3239-1
Type :
conf
DOI :
10.1109/CIT.2008.Workshops.108
Filename :
4568554
Link To Document :
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