International Journal of Intelligent Internet of Things Computing
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International Journal of Intelligent Internet of Things Computing (2 papers in press)
Intelligent Big Data Service for Meteorological Cloud Platform by Tao Huang, Jie Zhang, Shengjun Xue Abstract: With the acceleration of meteorological informationization, meteorological data has gradually become a typical industry big data. In view of the challenge of storage, index and processing, cloud computing technology provides the technical support for meteorological big data. We design a framework of meteorological big data service in the cloud computing environment which contains meteorological services, meteorological scientific research services and public meteorological services. As the most popular distributed processing technology, MapReduce is effectively used for distributed processing of meteorological big data. Finally, based on MapReduce, the daily meteorological data of Baoshan Station in Shanghai is analysed, and the statistical results and corresponding examples are provided. The application research of meteorological big data in the cloud environment can not only improve the overall meteorological service level, but also play a significant role in accelerating the meteorological informatisation process in the big data era. Keywords: Cloud Computing; Meteorological Mata; Meteorological Mervices; Hadoop; MapReduce.
A Distributed Data Processing Platform over Meteorological Big Data Using MapReduce by Tao Huang, Shengjun Xue, Xiang Li, Feng Luo Abstract: In the era of big data, the data of the meteorological departments grows explosively, which puts higher requirements on the real-time processing of meteorological big data. Besides, the efficient storage for the massive meteorological data has also attracted much attention from the meteorological departments. Therefore, in response to the urgent requirements of meteorological big data in processing and storage, a distributed data processing platform over meteorological big data using MapReduce is designed. Technically, the platform develops corresponding real-time strategies according to various data properties, and obtains meteorological big data in real-time from multiple channels. Based on the MapReduce real-time processing, we realise the distributed storage to store the meteorological big data in the platform. Overall, our platform improves the real-time, reliability, availability and the access efficiency of meteorological big data which is easy to expand and also has a good reference value for big data processing in other similar industries. Keywords: Meteorology; Big Data; MapReduce; Platform; Data processing; Storage. DOI: 10.1504/IJIITC.2019.10021642