《大数据:分析的来源和最佳实践

A. Luntovskyy, L. Globa
{"title":"《大数据:分析的来源和最佳实践","authors":"A. Luntovskyy, L. Globa","doi":"10.1109/UkrMiCo47782.2019.9165334","DOIUrl":null,"url":null,"abstract":"Cyber-PHY, IoT, sensor networks, Robotics (thick and server-less mobile applications), real-time network applications (thin clouds clients) can generate large arrays of unmanaged, weakly structured, and non-configured data of various types, known as \"Big Data\". With the acceleration of industrial development \"Industry 4.0\" processing of such data became considerably more complicated. However, so-called problem \"Big Data\" is hard to solve or resist nowadays! The paper discusses the Best Practises and Case Studies aimed to overcoming of the Big Data problematics.","PeriodicalId":6754,"journal":{"name":"2019 International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo)","volume":"30 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Big Data: Sources and Best Practices for Analytics\",\"authors\":\"A. Luntovskyy, L. Globa\",\"doi\":\"10.1109/UkrMiCo47782.2019.9165334\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cyber-PHY, IoT, sensor networks, Robotics (thick and server-less mobile applications), real-time network applications (thin clouds clients) can generate large arrays of unmanaged, weakly structured, and non-configured data of various types, known as \\\"Big Data\\\". With the acceleration of industrial development \\\"Industry 4.0\\\" processing of such data became considerably more complicated. However, so-called problem \\\"Big Data\\\" is hard to solve or resist nowadays! The paper discusses the Best Practises and Case Studies aimed to overcoming of the Big Data problematics.\",\"PeriodicalId\":6754,\"journal\":{\"name\":\"2019 International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo)\",\"volume\":\"30 1\",\"pages\":\"1-6\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/UkrMiCo47782.2019.9165334\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UkrMiCo47782.2019.9165334","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

网络物理、物联网、传感器网络、机器人(厚和无服务器的移动应用)、实时网络应用(瘦云客户端)可以生成大量非管理、弱结构和非配置的各种类型的数据,被称为“大数据”。随着工业发展的加速,“工业4.0”对这些数据的处理变得更加复杂。然而,所谓的“大数据”问题在当今是难以解决或难以抗拒的!本文讨论了旨在克服大数据问题的最佳实践和案例研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Big Data: Sources and Best Practices for Analytics
Cyber-PHY, IoT, sensor networks, Robotics (thick and server-less mobile applications), real-time network applications (thin clouds clients) can generate large arrays of unmanaged, weakly structured, and non-configured data of various types, known as "Big Data". With the acceleration of industrial development "Industry 4.0" processing of such data became considerably more complicated. However, so-called problem "Big Data" is hard to solve or resist nowadays! The paper discusses the Best Practises and Case Studies aimed to overcoming of the Big Data problematics.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信