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Emerging risk forecast system using associative index mining analysis

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Abstract

Accessibility to the information increased by the development of IT converging technology and therefore various information such as economy, society, technology, culture and etc are distributed without limit to time and place. Data mining that discovers useful information by analyzing the data that accumulated to a large amount is receiving spotlight. By the distribution of IoT machine, the collection of life log has been eased and the interest on personalized service fit to the used is increasing. Companies and nationals use various analysis methods to develop big data that is accumulated for the safety of its people and asset into simple index. Meteorological Administration analyze the weather data and provide service through internet and app by developing weather index analyzing the extent of influence of weather on life, health and industry. The weather index measure by each region by using data collected real time at the weather observation point. Weather index is classified into the life weather index, health weather index and industry weather index by fields and provide coping measures per steps along with precautions. The weather index provided at this present is a simple service that did not consider the correlation of the index. Also it has a problem of not considering the situation by the location of the user. This study suggests emerging risk forecast system using associative index mining analysis. The suggesting method is the monitoring system focused on the user by analyzing the associative index using data mining and forecasting emerging risk by reflecting the user situation information. Index data collects the weather index XML file by using the Rest call method through the Meteorological Administration. The collected weather index analyzed the association relationship and interrelationship between the index by using the Apriori algorithm and Pearson correlation coefficient of the data mining. User situation information collects the surrounding temperature and humidity using the Zigbee communication. Collected temperature and humidity is applied to the daily weather index and health weather index and calculated fit for the situation of the user and the industry weather index is calculated focused on the user by using the association relationship and interrelationship between the index. By using the calculated weather index focused on the user, emerging risk of the life, health and industry scope is forecasted. Emerging risk is classified into life emerging risk, health emerging risk, industry emerging risk by the forecast method and is divided into four steps of low, general, high and danger. Emerging risk forecast system using the associative index mining analysis suggested provides precaution followed in the emerging risk and provides service such as weather index, weather information, user situation information etc of the Meteorological Administration. The suggested emerging risk forecasting system is the user based monitoring simulation that calculates the weather index real-time.

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Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1A09917313).

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Authors and Affiliations

  1. Intelligent System Lab., Dept. of Computer Information Engineering, Sangji University, 83, Sangjidae-gil, Wonju-si, Gangwon-do, 26339, Korea

    Joo-Chang Kim

  2. School of Computer Information Engineering, Sangji University, 83, Sangjidae-gil, Wonju-si, Gangwon-do, 26339, Korea

    Kyungyong Chung

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  1. Joo-Chang Kim
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Correspondence to Kyungyong Chung.

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Kim, JC., Chung, K. Emerging risk forecast system using associative index mining analysis. Cluster Comput 20, 547–558 (2017). https://doi.org/10.1007/s10586-016-0702-6

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