Article(id=1242135065740779591, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242135049559154727, articleNumber=null, orderNo=11, doi=10.3981/j.issn.1000-7857.2016.14.006, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1464537600000, receivedDateStr=2016-05-30, revisedDate=1467043200000, revisedDateStr=2016-06-28, acceptedDate=null, acceptedDateStr=null, onlineDate=1471514247820, onlineDateStr=2016-08-18, pubDate=1469635200000, pubDateStr=2016-07-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1471514247820, onlineIssueDateStr=2016-08-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774077677423, creator=sys-migrate, updateTime=1774077677423, updator=sys-migrate, issue=Issue{id=1242135049559154727, tenantId=1146029695717560320, journalId=1146031591421210625, year='2016', volume='34', issue='14', pageStart='1', pageEnd='124', issueExtLink='null', onlineDate='null', pubDate='1469635200000', pubDateStr='2016-07-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774077673565, creator='sys-migrate', updateTime=1774077673565, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=49, endPage=59, ext={EN=ArticleExt(id=1242135068005707949, articleId=1242135065740779591, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Technological progress and trends of big data, columnId=1242133161577095994, journalTitle=Science & Technology Review, columnName=Special Issues, runingTitle=null, highlight=null, articleAbstract=With the rapid development of IT technology, worldwide businesses are experiencing informatization changes, and almost every industry is trying to exploit and utilize the value of big data. In order to take full advantage of the opportunity while effectively dealing with the challenges brought by big data, academia, industry and government actively promote the layout adjustment and establish the holistic strategic plan. This paper first introduces the background and dynamics of big data, and expounds the national policy environment and industry development status. Then it describes the technical progress on big data, with a comprehensive overview about the technology architecture and innovative features. Finally, the development trend is forecasted and some suggestions relating to big data visualization, multidisciplinary integration, security and privacy, depth analysis etc. are proposed., authors=CHENG Xueqi, JIN Xiaolong, YANG Jing, XU Jun, authorsList=CHENG Xueqi, JIN Xiaolong, YANG Jing, XU Jun, authorCompany=CAS Key Laboratory of Network Data Science & Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=NgXTH8luFcdQb8M0xmvmpw==, pdfFileSize=1244719, 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=1242135067141677133, articleId=1242135065740779591, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=大数据技术进展与发展趋势, columnId=1242116902361830275, journalTitle=科技导报, columnName=专题论文, runingTitle=null, highlight=null, articleAbstract=随着IT 技术的高速发展，世界范围内各行各业都在进行信息化变革，几乎每个行业都在努力发现和利用大数据的价值。为了充分利用大数据带来的机遇，同时有效应对大数据带来的挑战，国内外产业界、科学界和政府部门都在积极布局、制定战略规划。本文介绍大数据背景与动态，描述各国大数据政策实践及中国大数据发展的政策环境和产业界生态发展状况；阐述大数据技术的进展，梳理其生态体系和创新特点；提出大数据可视化、多学科融合、安全与隐私、深度分析等发展趋势和相关建议。, authors=程学旗, 靳小龙, 杨婧, 徐君, authorsList=程学旗, 靳小龙, 杨婧, 徐君, authorCompany=中国科学院计算技术研究所, 中国科学院网络数据科学与技术重点实验室, 北京 100190, correspAuthors=null, authorNote=程学旗,研究员,研究方向为网络科学与社会计算、互联网搜索与挖掘、网络信息安全等,电子信箱:cxq@ict.ac.cn, correspAuthorsNote=杨婧(通信作者),博士,研究方向为最优化查询、数据挖掘等,电子信箱:jyang@ict.ac.cn, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=/598mRQ/OhA2Abf64+QGzw==, pdfFileSize=1244719, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, 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