Article(id=1242138046271922718, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242138028983005327, articleNumber=null, orderNo=6, doi=10.3981/j.issn.1000-7857.2020.03.003, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1573228800000, receivedDateStr=2019-11-09, revisedDate=1579363200000, revisedDateStr=2020-01-19, acceptedDate=null, acceptedDateStr=null, onlineDate=1585714926537, onlineDateStr=2020-04-01, pubDate=1581523200000, pubDateStr=2020-02-13, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1585714926537, onlineIssueDateStr=2020-04-01, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774078388037, creator=sys-migrate, updateTime=1774078388037, updator=sys-migrate, issue=Issue{id=1242138028983005327, tenantId=1146029695717560320, journalId=1146031591421210625, year='2020', volume='38', issue='3', pageStart='1', pageEnd='216', issueExtLink='null', onlineDate='null', pubDate='1581523200000', pubDateStr='2020-02-13', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774078383914, creator='sys-migrate', updateTime=1774078383914, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=47, endPage=67, ext={EN=ArticleExt(id=1242138054459207908, articleId=1242138046271922718, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=On big data algebra: A formal analytic methodology for big data science and engineering, columnId=1242138034179747997, journalTitle=Science & Technology Review, columnName=Exclusive: Big data strategy, runingTitle=null, highlight=null, articleAbstract=Basic researches of big data science have triggered the emergence of mathematical theories of big data systems. This paper presents a rigorous analytic methodology for big data science and engineering known as Big Data Algebra (BDA). The mathematical models of big data science in BDA are formally elicited from common patterns and essences of a wide variety of big data systems. BDA reveals that any big data system is a Recursively Typed Hyperstructure (RTHS) beyond the traditional domain of pure numbers. It leads to a set of algebraic operators for big data modeling, analysis, and synthesis towards the denotational mathematical structure of BDA. The formal principles and properties of big data and their mathematical manipulations provide a theoretical framework of big data science as the basis for applications in big data engineering., authors=WANG Yingxu^1,2,3, JIN Jin², authorsList=WANG Yingxu, JIN Jin, authorCompany=1. National Engineering Key Lab for Big Data System Software, School of Software, Tsinghua University, Beijing 100084, China;
2. Beijing National Research Center of Information Science and Technology, Tsinghua University, Beijing 100084, China;
3. International Institute of Cognitive Informatics and Cognitive Computing(ICIC), Department of Electrical and Computer Engineering, Schulich School of Engineering and Hotchkiss Brain Institute, University of Calgary, Calgary T2N 1N4, Canada, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=dBJLhVWBdrOYYGgHFjTmqA==, pdfFileSize=1567428, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1242138053549040224, articleId=1242138046271922718, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=论大数据代数（BDA）：大数据科学与工程的分析方法, columnId=1242138033248612506, journalTitle=科技导报, columnName=专题：大数据战略, runingTitle=null, highlight=null, articleAbstract=大数据科学和系统的基础研究推动了大数据数学理论的产生。本文引出大数据科学和工程的一种严格分析方法：大数据代数（BDA）从提取各种大数据系统的共同模式中形式地导出大数据科学的数学模型。BDA揭示了任何大数据系统是一种超越传统纯数字的新型递归类型化超结构（RTHS）。基于大数据的递归超结构，创建了一组严格的代数算子，用于对大数据系统的建模、分析、综合和认知学习。这一基础研究建立了一个大数据科学的理论架构，其为解释大数据的原理和性质及其在大数据工程中的形式推理提供了一个方法论基础。, authors=WANG Yingxu^1,2,3, 靳瑾², authorsList=WANG Yingxu, 靳瑾, authorCompany=1. 清华大学大数据系统软件国家工程重点实验室, 北京 100084;
2. 清华大学北京信息科学与技术国家研究中心, 北京 100084;
3. 国际认知信息学与认知计算学会;卡尔加里大学电气与计算机工程系, Schulich工程学院, Hotchkiss脑科学研究所, 加拿大卡尔加里 T2N 1N4, correspAuthors=null, authorNote=WANG Yingxu,教授,研究方向为认知信息学、软件科学、大数据代数和指称数学,电子信箱:yingxu@ucalgary.ca, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=AD53SuXF0ekDgXpmVJqyRQ==, pdfFileSize=1567428, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=国家重点研发计划项目（2016YFB0501504）；国家自然科学基金项目（U1509213）)}, authors=null, keywords=[Keyword(id=1242138053125415515, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242138046271922718, language=CN, orderNo=1, keyword=大数据), Keyword(id=1242138053200912988, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242138046271922718, language=CN, orderNo=1, 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