{"title":"基于大数据分析的气象预报","authors":"Shadi A. Aljawarneh, J. A. L. Torralbo","doi":"10.1145/3460620.3460622","DOIUrl":null,"url":null,"abstract":"In this paper, we present the main ideas behind the development of a system that can be used to deal with meteorological big data. In particular, the system captures data online and downloads it locally onto a MongoDB database. After that, the user can create a particular database and corresponding minable views for analysis. The results provided by the systems are predictive models with the ability to predict some weather-related variables, such as temperature and rainfall. The system has been validated from a triple perspective (usability, experts’ validation, and performance assessment), obtaining satisfactory results. This paper aims to be a brief guide for authors who intend to developed similar systems either in the meteorological field or other domains generating big amounts of data.","PeriodicalId":36824,"journal":{"name":"Data","volume":"22 1","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Meteorological forecasting based on big data analysis\",\"authors\":\"Shadi A. Aljawarneh, J. A. L. Torralbo\",\"doi\":\"10.1145/3460620.3460622\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present the main ideas behind the development of a system that can be used to deal with meteorological big data. In particular, the system captures data online and downloads it locally onto a MongoDB database. After that, the user can create a particular database and corresponding minable views for analysis. The results provided by the systems are predictive models with the ability to predict some weather-related variables, such as temperature and rainfall. The system has been validated from a triple perspective (usability, experts’ validation, and performance assessment), obtaining satisfactory results. This paper aims to be a brief guide for authors who intend to developed similar systems either in the meteorological field or other domains generating big amounts of data.\",\"PeriodicalId\":36824,\"journal\":{\"name\":\"Data\",\"volume\":\"22 1\",\"pages\":\"\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2021-04-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data\",\"FirstCategoryId\":\"90\",\"ListUrlMain\":\"https://doi.org/10.1145/3460620.3460622\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1145/3460620.3460622","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Meteorological forecasting based on big data analysis
In this paper, we present the main ideas behind the development of a system that can be used to deal with meteorological big data. In particular, the system captures data online and downloads it locally onto a MongoDB database. After that, the user can create a particular database and corresponding minable views for analysis. The results provided by the systems are predictive models with the ability to predict some weather-related variables, such as temperature and rainfall. The system has been validated from a triple perspective (usability, experts’ validation, and performance assessment), obtaining satisfactory results. This paper aims to be a brief guide for authors who intend to developed similar systems either in the meteorological field or other domains generating big amounts of data.