A Design of Context Prediction Structure using Homogeneous Feature Extraction

Hyung-Sun Kim; Kyoung-Mi Im; Jae-Hyun Lim

A Design of Context Prediction Structure using Homogeneous Feature Extraction

Hyung-Sun Kim, Kyoung-Mi Im, Jae-Hyun Lim, Journal of Internet Computing and Services, Vol. 11, No. 4, pp. 85-94, Aug. 2010
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Keywords: prediction, location-prediction, SOFM, ARIMA, time series analysis

Abstract

In this paper, we propose a location-prediction structure that can provide user service in advance. It consists of seven steps and supplies intelligent services which can forecast user's location. Context information collected from physical sensors and a history database is so difficult that it can't present importance of data and abstraction of data because of heterogeneous data type. Hence, we offer the location-prediction that change data type from heterogeneous data to homogeneous data. Extracted data is clustered by SOFM, then it gets user's location information by ARIMA and realizes the services by a reasoning engine. In order to validate the proposed location-prediction, we built a test-bed and test it by the scenario.

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Cite this article

[APA Style]

Kim, H., Im, K., & Lim, J. (2010). A Design of Context Prediction Structure using Homogeneous Feature Extraction. Journal of Internet Computing and Services, 11(4), 85-94.

[IEEE Style]

H. Kim, K. Im, J. Lim, "A Design of Context Prediction Structure using Homogeneous Feature Extraction," Journal of Internet Computing and Services, vol. 11, no. 4, pp. 85-94, 2010.

[ACM Style]

Hyung-Sun Kim, Kyoung-Mi Im, and Jae-Hyun Lim. 2010. A Design of Context Prediction Structure using Homogeneous Feature Extraction. Journal of Internet Computing and Services, 11, 4, (2010), 85-94.