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Journal of Emerging Trends in Computing and Information Sciences >> Call for Papers Vol. 8 No. 3, March 2017

Journal of Emerging Trends in Computing and Information Sciences

Development of a Road Big-Data Storage Platform for Predicting the Driving Environment

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Author Jung-Ah Ha, Kyusoo Chong
ISSN 2079-8407
On Pages 14-22
Volume No. 7
Issue No. 1
Issue Date February 1, 2016
Publishing Date February 1, 2016
Keywords Hadoop, open API, big data, driving environment.


In this study, a platform that collects and stores various data on the roads to predict the driving environment was developed. Various open platforms were compared and analyzed for the development of a platform, and a road big-data storage platform was developed using Hortonworks, an open source distributed storage and processing platform. The collected data were classified into real-time data and non-real-time data according to their characteristics, and were stored in different ways. Non-real-time data were collected using Sqoop, which can take a large amount of data at a time, and the data that can be collected in real time through Open API were collected using Flume. The collected data were stored in the Hadoop-based HDFS from various processes, and were expressed on the web. Based on some collected data, it was found that the driving hours differed according to the precipitation, but it was believed that making an accurate prediction in a period which predict difficult requires data convergence. The platform that was developed in this study was a collection and storage model thus issued such as how to analyze the data collected through this system, how to predict the driving environment and what data is needed by users will addressed in the future.

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