⑴ 求一篇与大数据或者大数据信息安全专业相关的原版英文文献及其翻译,3000字左右。好人,拜托!

Big data refers to the huge volume of data that cannotbe stored and processed with in a time frame intraditional file system.The next question comes in mind is how big this dataneeds to be in order to classify as a big data. There is alot of misconception in referring a term big data. Weusually refer a data to be big if its size is in gigabyte,terabyte, Petabyte or Exabyte or anything larger thanthis size. This does not define a big data completely.Even a small amount of file can be referred to as a bigdata depending upon the content is being used.Let’s just take an example to make it clear. If we attacha 100 MB file to an email, we cannot be able to do so.As a email does not support an attachment of this size.Therefore with respect to an email, this 100mb filecan be referred to as a big data. Similarly if we want toprocess 1 TB of data in a given time frame, we cannotdo this with a traditional system since the resourcewith it is not sufficient to accomplish this task.As you are aware of various social sites such asFacebook, twitter, Google+, LinkedIn or YouTubecontains data in huge amount. But as the users aregrowing on these social sites, the storing and processingthe enormous data is becoming a challenging task.Storing this data is important for various firms togenerate huge revenue which is not possible with atraditional file system. Here is what Hadoop comes inthe existence.Big Data simply means that huge amountof structured, unstructured and semi-structureddata that has the ability to be processed for information. Now a days massive amount of dataproced because of growth in technology,digitalization and by a variety of sources, includingbusiness application transactions, videos, picture ,electronic mails, social media, and so on. So to processthese data the big data concept is introced.Structured data: a data that does have a proper formatassociated to it known as structured data. For examplethe data stored in database files or data stored in excelsheets.Semi-Structured Data: A data that does not have aproper format associated to it known as structured data.For example the data stored in mail files or in docx.files.Unstructured data: a data that does not have any formatassociated to it known as structured data. For examplean image files, audio files and video files.Big data is categorized into 3 v’s associated with it thatare as follows:[1]Volume: It is the amount of data to be generated i.e.in a huge quantity.Velocity: It is the speed at which the data gettinggenerated.Variety: It refers to the different kind data which isgenerated.A. Challenges Faced by Big DataThere are two main challenges faced by big data [2]i. How to store and manage huge volume of dataefficiently.ii. How do we process and extract valuableinformation from huge volume data within a giventime frame.These main challenges lead to the development ofhadoop framework.Hadoop is an open source framework developed byck cutting in 2006 and managed by the apachesoftware foundation. Hadoop was named after yellowtoy elephant.Hadoop was designed to store and process dataefficiently. Hadoop framework comprises of two maincomponents that are:i. HDFS: It stands for Hadoop distributed filesystem which takes care of storage of data withinhadoop cluster.ii. MAPREDUCE: it takes care of a processing of adata that is present in the HDFS.Now let’s just have a look on Hadoop cluster:Here in this there are two nodes that are Master Nodeand slave node.Master node is responsible for Name node and JobTracker demon. Here node is technical term used todenote machine present in the cluster and demon isthe technical term used to show the backgroundprocesses running on a Linux machine.The slave node on the other hand is responsible forrunning the data node and the task tracker demons.The name node and data node are responsible forstoring and managing the data and commonly referredto as storage node. Whereas the job tracker and tasktracker is responsible for processing and computing adata and commonly known as Compute node.Normally the name node and job tracker runs on asingle machine whereas a data node and task trackerruns on different machines.B. Features Of Hadoop:[3]i. Cost effective system: It does not require anyspecial hardware. It simply can be implementedin a common machine technically known ascommodity hardware.ii. Large cluster of nodes: A hadoop system cansupport a large number of nodes which providesa huge storage and processing system.iii. Parallel processing: a hadoop cluster provide theaccessibility to access and manage data parallelwhich saves a lot of time.iv. Distributed data: it takes care of splinting anddistributing of data across all nodes within a cluster.it also replicates the data over the entire cluster.v. Automatic failover management: once and AFMis configured on a cluster, the admin needs not toworry about the failed machine. Hadoop replicatesthe configuration Here one of each data iscopied or replicated to the node in the same rackand the hadoop take care of the internetworkingbetween two racks.vi. Data locality optimization: This is the mostpowerful thing of hadoop which make it the mostefficient feature. Here if a person requests for ahuge data which relies in some other place, themachine will sends the code of that data and thenother person compiles it and use it in particularas it saves a log to bandwidthvii. Heterogeneous cluster: node or machine can beof different vendor and can be working ondifferent flavor of operating systems.viii. Scalability: in hadoop adding a machine orremoving a machine does not effect on a cluster.Even the adding or removing the component ofmachine does not.C. Hadoop ArchitectureHadoop comprises of two componentsi. HDFSii. MAPREDUCEHadoop distributes big data in several chunks and storedata in several nodes within a cluster whichsignificantly reces the time.Hadoop replicates each part of data into each machinethat are present within the cluster.The no. of copies replicated depends on the replicationfactor. By default the replication factor is 3. Thereforein this case there are 3 copies to each data on 3 differentmachines。reference:Mahajan, P., Gaba, G., & Chauhan, N. S. (2016). Big Data Security. IITM Journal of Management and IT, 7(1), 89-94.自己拿去翻译网站翻吧,不懂可以问

⑵ 求一篇关于大数据的外文文献加翻译,翻译后的字数在3000到5000,或者其他关于数据库的也行,必重赏啊

童鞋你好!这个估计需要自己搜索了!网上基本很难找到免费给你服务的!我在这里给你点搜索国际上常用的外文数据库:———————————————————-❶ISI web of knowledge Engineering Village2❷Elsevier SDOL数据库 IEEE/IEE(IEL)❸EBSCOhost RSC英国皇家化学学会❹ACM美国计算机学会 ASCE美国土木工程师学会❺Springer电子期刊 WorldSciNet电子期刊全文库❻Nature周刊 NetLibrary电子图书❼ProQuest学位论文全文数据库❽国道外文专题数据库 CALIS西文期刊目次数据库❾推荐使用ISI web of knowledge Engineering Village2———————————————————–中文翻译得自己做了,实在不成就谷歌翻译。弄完之后,自己阅读几遍弄顺了就成啦!学校以及老师都不会看这个东西的!外文翻译不是论文的主要内容!所以,很容易过去的!祝你好运!

⑶ 外文文献导出一般什么格式带doi

外文文献导出一般有数字格式带doi。外文文献导出,发现参考文献找不全页码,可以将文章标题复制到中国知网、维普、万方、网络学术、谷粉学术数据库查找,找到以后导出参考文献格式即可。doi的全称是digital object unique identifier,是指数字对象唯一标识符,是云计算背景下最佳的“大数据”样本存储和应用技术,用于IKE进行协商SA协议统一分配。

⑷ Google公司三篇英文文献中的一个主要创新点

Google公司三篇英文文献中的一个主要创新点就是大数据技术的发展和应用。Google公司三篇英文文献分别是《Google File System》、《Google Bigtable》和《Google Map Rece》。其实描述的就是Google的三种技术,GFS分布式文件系统、Bigtable分布式数据存储系统、MapRece编程模型,都是基于分布式并行运行的,部署在大量普通机器组成的集群之上。它们相互之间都有相似之处,也能协调在一起运行和工作,三篇文章的重要目的就是解决分布式并行计算的问题,这也为大数据技术的发展和应用提供了可能。

⑸ 现在准备写大数据与人工智能的论文,后面投稿BDAI 2019国际会议,现在的情况是外文文献要如何翻译,有没


⑹ 常用的医药文献检索外文数据库有哪些




⑺ 如果我们的研究主题为大数据,应检索哪些文献

1.[期刊论文]数据科学与大数据技术专业的教材建设探索期刊:《新闻文化建设》 | 2021 年第 002 期摘要:随着大数据时代的到来,信息技术蓬勃发展,国家大力推进大数据产业的发展,鼓励高校设立数据科学和数据工程相关专业。在趋势的推动下,许多高校成立了数据科学与大数据技术专业。本文通过研究数据科学与大数据技术专业的发展现状,探索新专业下人才培养的课程设置及教材建设等问题,同时介绍高等教育出版社在数据科学与大数据技术专业教材建设方面的研发成果。关键词:数据科学与大数据技术专业;课程设置;教材建设链接:https://www.zhangqiaokeyan.com/academic-journal-cn_detail_thesis/0201289060336.html—————————————————————————————————2.[期刊论文]数据科学与大数据技术专业课程体系探索期刊:《科教文汇》 | 2021 年第 002 期摘要:该文阐述了数据科学与大数据专业的设置必要性、专业的培养目标和知识能力结构,最后探索了数据科学与大数据专业的技术性课程体系设置方法.希望该文内容对数据科学与大数据技术专业的培养方案制订和课程体系构造具有一定的指导意义和参考价值.关键词:数据科学;大数据技术;课程体系链接:https://www.zhangqiaokeyan.com/academic-journal-cn_science-ecation-article-collects_thesis/0201284684572.html—————————————————————————————————3.[期刊论文]数据科学与大数据技术专业实验实践教学探析期刊:《长春大学学报(自然科学版)》 | 2021 年第 001 期摘要:近些年各种信息数据呈爆炸式增长,在这种背景下,国家在2015年印发了关于大数据技术人才培养的相关文件,每年多个高校的大数据相关专业获批.数据量的增长对数据处理的要求越来越高,各行业涉及信息数据的范围越来越广,对大数据专业人才的需求越来越多.为了应对社会需求,如何科学地规划数据科学与大数据专业的本科教育,尤其在当前注重实践操作的背景下,如何制定适合的实验实践教学方案,更好满足社会需求.关键词:数据科学;大数据;实践教学链接:https://www.zhangqiaokeyan.com/academic-journal-cn_journal-changchun-university_thesis/0201288750604.html

⑻ Cnki大数据研究平台包含哪些数据


⑼ 医学外文文献什么数据库好

使用UTH国际的芝麻秘语翻译软件吧。芝麻秘语是 UTH 国际旗下的一款具有较高影响力的跨语言即时互译工具,以其极简、极速、极致的特点支持多格式文档的智能翻译,且自带翻译记忆功能。大量用户的实践和反馈证明,芝麻秘语广泛适用于高校硕博士生和科研人员等用户群体的产学研需求。对于广大高校师生,尤其是硕博士生而言,海量阅读各种语言的学术论文、专业文献是学习和研究的必修课,在这个过程中攻克语言壁垒成为一大难题。 在芝麻秘语中,只需轻点鼠标,将文档拖拽至悬浮窗内,即可在10-15秒内快速输出译文,同时完整保留源文档的文件格式和页面排版,实现同格式下的快速阅读。 芝麻秘语支持25种语言和 Word、Excel、PPT 等12 种格式的文档解析,可同步处理5个不同格式的文档翻译,较于以往仅靠逐字逐句复制黏贴获取翻译的常规工具而言,在提高效率节省翻译时间上具有显著的优势。 在翻译方面,芝麻秘语根植于UTH国际专有的百亿级句对的人工精准翻译大数据,辅以谷歌翻译API数据调用,确保其平均翻译质量优于传统翻译方式。更为重要的是,借助于共用的语料大数据后台,芝麻秘语还通过UTH国际的语料库电商平台——“译猫网”,不断聚集来自全球翻译人员和语言服务提供商的多样化且不断更新的鲜活的语料,从而确保用户在使用中明显体验到翻译质量的持续提升。