Leveraging Big Data and Machine Learning for Digital Transformation

Jingwei Huang
{"title":"Leveraging Big Data and Machine Learning for Digital Transformation","authors":"Jingwei Huang","doi":"10.3233/jid190020","DOIUrl":null,"url":null,"abstract":"The fast diffusion of new technologies such as the Internet of Things (IoT), Cloud Computing, Wireless Communication, Mobile Computing, Big Data, AI (including Machine Learning), Cyber-Physical Systems, Blockchain, and others, has been leading to a pervasive digital transformation (Foster, 2020; Huang, 2017). In this transformation, many traditional artifacts and business processes are digitalized. Examples include digital medical records, digital media, digital currencies, digital twin, digital manufacturing, and digital engineering (US DoD, 2018). Beyond the digital forms of artifacts and processes and the associated big data phenomenon, the digitalization enables fast dissemination of digital information and much-enhanced knowledge sharing, enables virtualization of various services, enables innovations by leveraging digital technologies, and fosters the creation of new “data products” and “digital knowledge products”. Digital transformation is reshaping the landscape of systems design (Huang et al., 2020). Digital transformation and the associated technologies are efficiently and effectively helping the world to cope with the challenging Covid-19 pandemic. On the other hand, they have also been impacting many aspects of the fast-transforming human society and lead to many issues to deal, to think, and to explore. In today’s rapid-changing and highly competitive environment with an unprecedented richness of information, it is an essential capability to turn big data into knowledge, in order to support effective and efficient decision making and operations processes, no matter in a field of scientific research, engineering, business, healthcare, public services, or others. In the transformation of data into knowledge, machine learning and data analytics play a central role, where efficient big data platforms are fundamental for handling the big data. This issue of JIDPS presents four papers, reflecting research topics on the aspects of deep neural","PeriodicalId":342559,"journal":{"name":"J. Integr. Des. Process. Sci.","volume":"226 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"J. Integr. Des. Process. Sci.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/jid190020","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

The fast diffusion of new technologies such as the Internet of Things (IoT), Cloud Computing, Wireless Communication, Mobile Computing, Big Data, AI (including Machine Learning), Cyber-Physical Systems, Blockchain, and others, has been leading to a pervasive digital transformation (Foster, 2020; Huang, 2017). In this transformation, many traditional artifacts and business processes are digitalized. Examples include digital medical records, digital media, digital currencies, digital twin, digital manufacturing, and digital engineering (US DoD, 2018). Beyond the digital forms of artifacts and processes and the associated big data phenomenon, the digitalization enables fast dissemination of digital information and much-enhanced knowledge sharing, enables virtualization of various services, enables innovations by leveraging digital technologies, and fosters the creation of new “data products” and “digital knowledge products”. Digital transformation is reshaping the landscape of systems design (Huang et al., 2020). Digital transformation and the associated technologies are efficiently and effectively helping the world to cope with the challenging Covid-19 pandemic. On the other hand, they have also been impacting many aspects of the fast-transforming human society and lead to many issues to deal, to think, and to explore. In today’s rapid-changing and highly competitive environment with an unprecedented richness of information, it is an essential capability to turn big data into knowledge, in order to support effective and efficient decision making and operations processes, no matter in a field of scientific research, engineering, business, healthcare, public services, or others. In the transformation of data into knowledge, machine learning and data analytics play a central role, where efficient big data platforms are fundamental for handling the big data. This issue of JIDPS presents four papers, reflecting research topics on the aspects of deep neural
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用大数据和机器学习实现数字化转型
物联网(IoT)、云计算、无线通信、移动计算、大数据、人工智能(包括机器学习)、网络物理系统、区块链等新技术的快速扩散,已经导致了一场无处不在的数字化转型(Foster, 2020;黄,2017)。在此转换中,许多传统工件和业务流程被数字化。例子包括数字医疗记录、数字媒体、数字货币、数字孪生、数字制造和数字工程(美国国防部,2018年)。除了工件和流程的数字化形式以及相关的大数据现象之外,数字化还使数字信息的快速传播和知识共享得到极大加强,使各种服务得以虚拟化,使利用数字技术进行创新成为可能,并促进创造新的“数据产品”和“数字知识产品”。数字化转型正在重塑系统设计的格局(Huang et al., 2020)。数字化转型和相关技术正在高效地帮助世界应对具有挑战性的Covid-19大流行。另一方面,它们也影响着快速变革的人类社会的许多方面,并导致许多问题需要处理、思考和探索。在当今瞬息万变、竞争激烈、信息空前丰富的环境中,无论是在科研、工程、商业、医疗保健、公共服务还是其他领域,为了支持有效和高效的决策和运营流程,将大数据转化为知识是一项必不可少的能力。在将数据转化为知识的过程中,机器学习和数据分析发挥着核心作用,高效的大数据平台是处理大数据的基础。本期JIDPS收录了四篇论文,反映了深度神经网络方面的研究课题
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
The need for innovations in healthcare systems using patient experience and advancing information technology An Investigation into the Development of Convergence Engineering Digital Engineering Transformation with Trustworthy AI towards Industry 4.0: Emerging Paradigm Shifts Footsteps Towards a Transdisciplinary Design and Process Science THE RELATIVISTIC OBSERVER: Consequences of a Linear Expansion of Spacetime
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1