数据库外文文献翻译
毕业论文(设计)外文文献翻译及原文
金融体制、融资约束与投资——来自OECD的实证分析R．SemenovDepartment of Economics，University of Nijmegen，Nijmegen（荷兰内梅亨大学，经济学院）这篇论文考查了OECD的11个国家中现金流量对企业投资的影响.我们发现不同国家之间投资对企业内部可获取资金的敏感性具有显著差异,并且银企之间具有明显的紧密关系的国家的敏感性比银企之间具有公平关系的国家的低.同时，我们发现融资约束与整体金融发展指标不存在关系.我们的结论与资本市场信息和激励问题对企业投资具有重要作用这种观点一致，并且紧密的银企关系会减少这些问题从而增加企业获取外部融资的渠道。
一、引言各个国家的企业在显著不同的金融体制下运行。
金融发展水平的差别(例如，相对GDP的信用额度和相对GDP的相应股票市场的资本化程度），在所有者和管理者关系、企业和债权人的模式中，企业控制的市场活动水平可以很好地被记录.在完美资本市场,对于具有正的净现值投资机会的企业将一直获得资金。
然而，经济理论表明市场摩擦，诸如信息不对称和激励问题会使获得外部资本更加昂贵，并且具有盈利投资机会的企业不一定能够获取所需资本.这表明融资要素，例如内部产生资金数量、新债务和权益的可得性，共同决定了企业的投资决策.现今已经有大量考查外部资金可得性对投资决策的影响的实证资料（可参考，例如Fazzari（1998）、 Hoshi(1991)、 Chapman（1996）、Samuel(1998））.大多数研究结果表明金融变量例如现金流量有助于解释企业的投资水平。
这项研究结果解释表明企业投资受限于外部资金的可得性。
很多模型强调运行正常的金融中介和金融市场有助于改善信息不对称和交易成本，减缓不对称问题,从而促使储蓄资金投着长期和高回报的项目,并且提高资源的有效配置（参看Levine（1997)的评论文章）。
因而我们预期用于更加发达的金融体制的国家的企业将更容易获得外部融资.几位学者已经指出建立企业和金融中介机构可进一步缓解金融市场摩擦。
大数据外文翻译参考文献综述
大数据外文翻译参考文献综述(文档含中英文对照即英文原文和中文翻译)原文：Data Mining and Data PublishingData mining is the extraction of vast interesting patterns or knowledge from huge amount of data. The initial idea of privacy-preserving data mining PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. Privacy-preserving data mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed even to the partyrunning the algorithm. In contrast, privacy-preserving data publishing (PPDP) may not necessarily be tied to a specific data mining task, and the data mining task may be unknown at the time of data publishing. PPDP studies how to transform raw data into a version that is immunized against privacy attacks but that still supports effective data mining tasks. Privacy-preserving for both data mining (PPDM) and data publishing (PPDP) has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. One well studied approach is the k-anonymity model [1] which in turn led to other models such as confidence bounding, l-diversity, t-closeness, (α,k)-anonymity, etc. In particular, all known mechanisms try to minimize information loss and such an attempt provides a loophole for attacks. The aim of this paper is to present a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explain their effects on Data Privacy.Although data mining is potentially useful, many data holders are reluctant to provide their data for data mining for the fear of violating individual privacy. In recent years, study has been made to ensure that the sensitive information of individuals cannot be identified easily.Anonymity Models, k-anonymization techniques have been the focus of intense research in the last few years. In order to ensure anonymization of data while at the same time minimizing the informationloss resulting from data modifications, everal extending models are proposed, which are discussed as follows.1.k-Anonymityk-anonymity is one of the most classic models, which technique that prevents joining attacks by generalizing and/or suppressing portions of the released microdata so that no individual can be uniquely distinguished from a group of size k. In the k-anonymous tables, a data set is k-anonymous (k ≥ 1) if each record in the data set is in- distinguishable from at least (k . 1) other records within the same data set. The larger the value of k, the better the privacy is protected. k-anonymity can ensure that individuals cannot be uniquely identified by linking attacks.2. Extending ModelsSince k-anonymity does not provide sufficient protection against attribute disclosure. The notion of l-diversity attempts to solve this problem by requiring that each equivalence class has at least l well-represented value for each sensitive attribute. The technology of l-diversity has some advantages than k-anonymity. Because k-anonymity dataset permits strong attacks due to lack of diversity in the sensitive attributes. In this model, an equivalence class is said to have l-diversity if there are at least l well-represented value for the sensitive attribute. Because there are semantic relationships among the attribute values, and different values have very different levels of sensitivity. Afteranonymization, in any equivalence class, the frequency (in fraction) of a sensitive value is no more than α.3. Related Research AreasSeveral polls show that the public has an in- creased sense of privacy loss. Since data mining is often a key component of information systems, homeland security systems, and monitoring and surveillance systems, it gives a wrong impression that data mining is a technique for privacy intrusion. This lack of trust has become an obstacle to the benefit of the technology. For example, the potentially beneficial data mining re- search project, Terrorism Information Awareness (TIA), was terminated by the US Congress due to its controversial procedures of collecting, sharing, and analyzing the trails left by individuals. Motivated by the privacy concerns on data mining tools, a research area called privacy-reserving data mining (PPDM) emerged in 2000. The initial idea of PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. The solutions were often tightly coupled with the data mining algorithms under consideration. In contrast, privacy-preserving data publishing (PPDP) may not necessarily tie to a specific data mining task, and the data mining task is sometimes unknown at the time of data publishing. Furthermore, some PPDP solutions emphasize preserving the datatruthfulness at the record level, but PPDM solutions often do not preserve such property. PPDP Differs from PPDM in Several Major Ways as Follows ：1) PPDP focuses on techniques for publishing data, not techniques for data mining. In fact, it is expected that standard data mining techniques are applied on the published data. In contrast, the data holder in PPDM needs to randomize the data in such a way that data mining results can be recovered from the randomized data. To do so, the data holder must understand the data mining tasks and algorithms involved. This level of involvement is not expected of the data holder in PPDP who usually is not an expert in data mining.2) Both randomization and encryption do not preserve the truthfulness of values at the record level; therefore, the released data are basically meaningless to the recipients. In such a case, the data holder in PPDM may consider releasing the data mining results rather than the scrambled data.3) PPDP primarily “anonymizes” the data by hiding the identity of record owners, whereas PPDM seeks to directly hide the sensitive data. Excellent surveys and books in randomization and cryptographic techniques for PPDM can be found in the existing literature. A family of research work called privacy-preserving distributed data mining (PPDDM) aims at performing some data mining task on a set of private databasesowned by different parties. It follows the principle of Secure Multiparty Computation (SMC), and prohibits any data sharing other than the final data mining result. Clifton et al. present a suite of SMC operations, like secure sum, secure set union, secure size of set intersection, and scalar product, that are useful for many data mining tasks. In contrast, PPDP does not perform the actual data mining task, but concerns with how to publish the data so that the anonymous data are useful for data mining. We can say that PPDP protects privacy at the data level while PPDDM protects privacy at the process level. They address different privacy models and data mining scenarios. In the field of statistical disclosure control (SDC), the research works focus on privacy-preserving publishing methods for statistical tables. SDC focuses on three types of disclosures, namely identity disclosure, attribute disclosure, and inferential disclosure. Identity disclosure occurs if an adversary can identify a respondent from the published data. Revealing that an individual is a respondent of a data collection may or may not violate confidentiality requirements. Attribute disclosure occurs when confidential information about a respondent is revealed and can be attributed to the respondent. Attribute disclosure is the primary concern of most statistical agencies in deciding whether to publish tabular data. Inferential disclosure occurs when individual information can be inferred with high confidence from statistical information of the published data.Some other works of SDC focus on the study of the non-interactive query model, in which the data recipients can submit one query to the system. This type of non-interactive query model may not fully address the information needs of data recipients because, in some cases, it is very difficult for a data recipient to accurately construct a query for a data mining task in one shot. Consequently, there are a series of studies on the interactive query model, in which the data recipients, including adversaries, can submit a sequence of queries based on previously received query results. The database server is responsible to keep track of all queries of each user and determine whether or not the currently received query has violated the privacy requirement with respect to all previous queries. One limitation of any interactive privacy-preserving query system is that it can only answer a sublinear number of queries in total; otherwise, an adversary (or a group of corrupted data recipients) will be able to reconstruct all but 1 . o(1) fraction of the original data, which is a very strong violation of privacy. When the maximum number of queries is reached, the query service must be closed to avoid privacy leak. In the case of the non-interactive query model, the adversary can issue only one query and, therefore, the non-interactive query model cannot achieve the same degree of privacy defined by Introduction the interactive model. One may consider that privacy-reserving data publishing is a special case of the non-interactivequery model.This paper presents a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explains their effects on Data Privacy. k-anonymity is used for security of respondents identity and decreases linking attack in the case of homogeneity attack a simple k-anonymity model fails and we need a concept which prevent from this attack solution is l-diversity. All tuples are arranged in well represented form and adversary will divert to l places or on l sensitive attributes. l-diversity limits in case of background knowledge attack because no one predicts knowledge level of an adversary. It is observe that using generalization and suppression we also apply these techniques on those attributes which doesn’t need th is extent of privacy and this leads to reduce the precision of publishing table. e-NSTAM (extended Sensitive Tuples Anonymity Method) is applied on sensitive tuples only and reduces information loss, this method also fails in the case of multiple sensitive tuples.Generalization with suppression is also the causes of data lose because suppression emphasize on not releasing values which are not suited for k factor. Future works in this front can include defining a new privacy measure along with l-diversity for multiple sensitive attribute and we will focus to generalize attributes without suppression using other techniques which are used to achieve k-anonymity because suppression leads to reduce the precision ofpublishing table.译文：数据挖掘和数据发布数据挖掘中提取出大量有趣的模式从大量的数据或知识。
数据库中英文对照外文翻译文献
中英文对照外文翻译Database Management SystemsA database (sometimes spelled data base) is also called an electronic database , referring to any collection of data, or information, that is specially organized for rapid search and retrieval by a computer. Databases are structured to facilitate the storage, retrieval , modification, and deletion of data in conjunction with various data-processing operations .Databases can be stored on magnetic disk or tape, optical disk, or some other secondary storage device.A database consists of a file or a set of files. The information in these files may be broken down into records, each of which consists of one or more fields. Fields are the basic units of data storage , and each field typically contains information pertaining to one aspect or attribute of the entity described by the database . Using keywords and various sorting commands, users can rapidly search , rearrange, group, and select the fields in many records to retrieve or create reports on particular aggregate of data.Complex data relationships and linkages may be found in all but the simplest databases .The system software package that handles the difficult tasks associated with creating ,accessing, and maintaining database records is called a database management system(DBMS).The programs in a DBMS package establish an interface between the database itself and the users of the database.. (These users may be applications programmers, managers and others with information needs, and various OS programs.)A DBMS can organize, process, and present selected data elements form the database. This capability enables decision makers to search, probe, and query database contents in order to extract answers to nonrecurring and unplanned questions that aren’t available in regular reports. These questions might initially be vague and/or poorly defined ,but people can “browse” through the database until they have the needed information. In short, the DBMS will “manage” the stored data items and assemble the needed items from the common database in response to the queries of those who aren’t programmers.A database management system (DBMS) is composed of three major parts:(1)a storage subsystemthat stores and retrieves data in files;(2) a modeling and manipulation subsystem that provides the means with which to organize the data and to add , delete, maintain, and update the data;(3)and an interface between the DBMS and its users. Several major trends are emerging that enhance the value and usefulness of database management systems;Managers: who require more up-to-data information to make effective decisionCustomers: who demand increasingly sophisticated information services and more current information about the status of their orders, invoices, and accounts.Users: who find that they can develop custom applications with database systems in a fraction of the time it takes to use traditional programming languages.Organizations : that discover information has a strategic value; they utilize their database systems to gain an edge over their competitors.The Database ModelA data model describes a way to structure and manipulate the data in a database. The structural part of the model specifies how data should be represented(such as tree, tables, and so on ).The manipulative part of the model specifies the operation with which to add, delete, display, maintain, print, search, select, sort and update the data.Hierarchical ModelThe first database management systems used a hierarchical model-that is-they arranged records into a tree structure. Some records are root records and all others have unique parent records. The structure of the tree is designed to reflect the order in which the data will be used that is ,the record at the root of a tree will be accessed first, then records one level below the root ,and so on.The hierarchical model was developed because hierarchical relationships are commonly found in business applications. As you have known, an organization char often describes a hierarchical relationship: top management is at the highest level, middle management at lower levels, and operational employees at the lowest levels. Note that within a strict hierarchy, each level of management may have many employees or levels of employees beneath it, but each employee has only one manager. Hierarchical data are characterized by this one-to-many relationship among data.In the hierarchical approach, each relationship must be explicitly defined when the database is created. Each record in a hierarchical database can contain only one key field and only one relationship is allowed between any two fields. This can create a problem because data do not always conform to such a strict hierarchy.Relational ModelA major breakthrough in database research occurred in 1970 when E. F. Codd proposed a fundamentally different approach to database management called relational model ,which uses a table asits data structure.The relational database is the most widely used database structure. Data is organized into related tables. Each table is made up of rows called and columns called fields. Each record contains fields of data about some specific item. For example, in a table containing information on employees, a record would contain fields of data such as a person’s last name ,first name ,and street address.Structured query language(SQL)is a query language for manipulating data in a relational database .It is nonprocedural or declarative, in which the user need only specify an English-like description that specifies the operation and the described record or combination of records. A query optimizer translates the description into a procedure to perform the database manipulation.Network ModelThe network model creates relationships among data through a linked-list structure in which subordinate records can be linked to more than one parent record. This approach combines records with links, which are called pointers. The pointers are addresses that indicate the location of a record. With the network approach, a subordinate record can be linked to a key record and at the same time itself be a key record linked to other sets of subordinate records. The network mode historically has had a performance advantage over other database models. Today , such performance characteristics are only important in high-volume ,high-speed transaction processing such as automatic teller machine networks or airline reservation system.Both hierarchical and network databases are application specific. If a new application is developed ,maintaining the consistency of databases in different applications can be very difficult. For example, suppose a new pension application is developed .The data are the same, but a new database must be created.Object ModelThe newest approach to database management uses an object model , in which records are represented by entities called objects that can both store data and provide methods or procedures to perform specific tasks.The query language used for the object model is the same object-oriented programming language used to develop the database application .This can create problems because there is no simple , uniform query language such as SQL . The object model is relatively new, and only a few examples of object-oriented database exist. It has attracted attention because developers who choose an object-oriented programming language want a database based on an object-oriented model. Distributed DatabaseSimilarly , a distributed database is one in which different parts of the database reside on physically separated computers . One goal of distributed databases is the access of informationwithout regard to where the data might be stored. Keeping in mind that once the users and their data are separated , the communication and networking concepts come into play .Distributed databases require software that resides partially in the larger computer. This software bridges the gap between personal and large computers and resolves the problems of incompatible data formats. Ideally, it would make the mainframe databases appear to be large libraries of information, with most of the processing accomplished on the personal computer.A drawback to some distributed systems is that they are often based on what is called a mainframe-entire model , in which the larger host computer is seen as the master and the terminal or personal computer is seen as a slave. There are some advantages to this approach . With databases under centralized control , many of the problems of data integrity that we mentioned earlier are solved . But today’s personal computers, departmental computers, and distributed processing require computers and their applications to communicate with each other on a more equal or peer-to-peer basis. In a database, the client/server model provides the framework for distributing databases.One way to take advantage of many connected computers running database applications is to distribute the application into cooperating parts that are independent of one anther. A client is an end user or computer program that requests resources across a network. A server is a computer running software that fulfills those requests across a network . When the resources are data in a database ,the client/server model provides the framework for distributing database.A file serve is software that provides access to files across a network. A dedicated file server is a single computer dedicated to being a file server. This is useful ,for example ,if the files are large and require fast access .In such cases, a minicomputer or mainframe would be used as a file server. A distributed file server spreads the files around on individual computers instead of placing them on one dedicated computer.Advantages of the latter server include the ability to store and retrieve files on other computers and the elimination of duplicate files on each computer. A major disadvantage , however, is that individual read/write requests are being moved across the network and problems can arise when updating files. Suppose a user requests a record from a file and changes it while another user requests the same record and changes it too. The solution to this problems called record locking, which means that the first request makes others requests wait until the first request is satisfied . Other users may be able to read the record, but they will not be able to change it .A database server is software that services requests to a database across a network. For example, suppose a user types in a query for data on his or her personal computer . If the application is designed with the client/server model in mind ,the query language part on the personal computer simple sends the query across the network to the database server and requests to be notified when the data are found.Examples of distributed database systems can be found in the engineering world. Sun’s Network Filing System(NFS),for example, is used in computer-aided engineering applications to distribute data among the hard disks in a network of Sun workstation.Distributing databases is an evolutionary step because it is logical that data should exist at the location where they are being used . Departmental computers within a large corporation ,for example, should have data reside locally , yet those data should be accessible by authorized corporate management when they want to consolidate departmental data . DBMS software will protect the security and integrity of the database , and the distributed database will appear to its users as no different from the non-distributed database .In this information age, the data server has become the heart of a company. This one piece of software controls the rhythm of most organizations and is used to pump information lifeblood through the arteries of the network. Because of the critical nature of this application, the data server is also the one of the most popular targets for hackers. If a hacker owns this application, he can cause the company's "heart" to suffer a fatal arrest.Ironically, although most users are now aware of hackers, they still do not realize how susceptible their database servers are to hack attacks. Thus, this article presents a description of the primary methods of attacking database servers (also known as SQL servers) and shows you how to protect yourself from these attacks.You should note this information is not new. Many technical white papers go into great detail about how to perform SQL attacks, and numerous vulnerabilities have been posted to security lists that describe exactly how certain database applications can be exploited. This article was written for the curious non-SQL experts who do not care to know the details, and as a review to those who do use SQL regularly.What Is a SQL Server?A database application is a program that provides clients with access to data. There are many variations of this type of application, ranging from the expensive enterprise-level Microsoft SQL Server to the free and open source mySQL. Regardless of the flavor, most database server applications have several things in common.First, database applications use the same general programming language known as SQL, or Structured Query Language. This language, also known as a fourth-level language due to its simplistic syntax, is at the core of how a client communicates its requests to the server. Using SQL in its simplest form, a programmer can select, add, update, and delete information in a database. However, SQL can also be used to create and design entire databases, perform various functions on the returned information, and even execute other programs.To illustrate how SQL can be used, the following is an example of a simple standard SQL query and a more powerful SQL query:Simple: "Select * from dbFurniture.tblChair"This returns all information in the table tblChair from the database dbFurniture.Complex: "EXEC master..xp_cmdshell 'dir c:\'"This short SQL command returns to the client the list of files and folders under the c:\ directory of the SQL server. Note that this example uses an extended stored procedure that is exclusive to MS SQL Server.The second function that database server applications share is that they all require some form of authenticated connection between client and host. Although the SQL language is fairly easy to use, at least in its basic form, any client that wants to perform queries must first provide some form of credentials that will authorize the client; the client also must define the format of the request and response.This connection is defined by several attributes, depending on the relative location of the client and what operating systems are in use. We could spend a whole article discussing various technologies such as DSN connections, DSN-less connections, RDO, ADO, and more, but these subjects are outside the scope of this article. If you want to learn more about them, a little Google'ing will provide you with more than enough information. However, the following is a list of the more common items included in a connection request.Database sourceRequest typeDatabaseUser IDPasswordBefore any connection can be made, the client must define what type of database server it is connecting to. This is handled by a software component that provides the client with the instructions needed to create the request in the correct format. In addition to the type of database, the request type can be used to further define how the client's request will be handled by the server. Next comes the database name and finally the authentication information.All the connection information is important, but by far the weakest link is the authentication information—or lack thereof. In a properly managed server, each database has its own users with specifically designated permissions that control what type of activity they can perform. For example, a user account would be set up as read only for applications that need to only access information. Another account should be used for inserts or updates, and maybe even a third account would be used for deletes.This type of account control ensures that any compromised account is limited in functionality. Unfortunately, many database programs are set up with null or easy passwords, which leads to successful hack attacks.译文数据库管理系统介绍数据库（database,有时拼作data base）又称为电子数据库，是专门组织起来的一组数据或信息，其目的是为了便于计算机快速查询及检索。
英文论文(外文文献)翻译成中文的格式与方法
英文论文（外文文献）翻译成中文的格式与方法英文论文（外文文献）翻译成中文的格式与方法本文关键词：外文，英文，中文，翻译成，文献英文论文（外文文献）翻译成中文的格式与方法本文简介：在撰写毕业设计（论文）或科研论文时，需要参考一些相关外文文献，了解国外的最新研究进展，这就需要我们找到最新最具代表性的外文文献，进行翻译整理，以备论文写作时参考，外文文献中英文文献占绝大多数，因此英文论文准确的翻译成中文就显得尤为重要！一、外文文献从哪里下载1、从知网国际文献总库中找英文论文（外文文献）翻译成中文的格式与方法本文内容：在撰写毕业设计（论文）或科研论文时，需要参考一些相关外文文献，了解国外的最新研究进展，这就需要我们找到最新最具代表性的外文文献，进行翻译整理，以备论文写作时参考，外文文献中英文文献占绝大多数，因此英文论文准确的翻译成中文就显得尤为重要！一、外文文献从哪里下载1、从知网国际文献总库中找，该数据库中包含14,000多家国外出版社的文献，囊括所有专业的英文文献资料。
2、一些免费的外文数据库或网站，为了方便大家查找，编者整理成文档供大家下载：国外免费文献数据库大全下载3、谷歌学术检索工具，检索时设置成只检索英文文献，键入与专业相关的关键词即可检索。
二、英文论文翻译格式与要求翻译的外文文献的字符要求不少于1.5万（或翻译成中文后至少在3000字以上）。
字数达到的文献一篇即可。
翻译的外文文献应主要选自学术期刊、学术会议的文章、有关着作及其他相关材料，应与毕业论文（设计）主题相关，并作为外文参考文献列入毕业论文（设计）的参考文献。
并在每篇中文译文首页用＂脚注＂形式注明原文作者及出处，中文译文后应附外文原文。
需认真研读和查阅术语完成翻译，不得采用翻译软件翻译。
中文译文的编排结构与原文同，撰写格式参照毕业论文的格式要求。
参考文献不必翻译，直接使用原文的（字体，字号，标点符号等与毕业论文中的参考文献要求同），参考文献的序号应标注在译文中相应的地方。
外文文献查找方法及翻译要求
文献查找：根据自己课题到知网或类似的数据库网站查找，有相关英文文章可以进行下载之后翻译。
如果不能下载，网站的中英文摘要一般都可以看到，根据英文关键词到谷歌搜索里面搜相关的英文文章。
要求的翻译原文不一定是专业的科技文章，只要内容相关，难度跟篇幅合适都可以拿来用。
如果想翻译专业的英文文献，可以通过谷歌的学术搜索网站/schhp?hl=zh-CN，给出的链接比较多，多数是链接到数据库上，没有账号不能下载，但是有些时候搜索出来的条目会有PDF链接，直接点击就能下载下来。
还可以通过维基百科查找相关词条的英文解释，/wiki/Main_Page，但无论英文还是中文都要符合模板中的格式要求，如果英文原文不能编辑，可以不作处理。
英译中：翻译一段检查一段，中文语言一定要通顺，不要有漏译。
不知道的单词一定要查，不要猜。
另一个是，同一个单词在文章里多次出现，每次翻译的时候都要用同样的单词翻译，不能原文同一个单词，在译文里出现的时候是不同的单词。
格式和原文要完全一样，原文有表格，就在译文里也画表格，原文是图片，译文也是图片，原文图片里文字的位置和译文图片里文字的位置要一致。
还有，整个文章统一字体。
查词顺序：先查英语国语字典或者用GOOGLE英文网页，理解英文单词的意思，然后用翻译软件英译中翻译，用翻译软件翻译出来的中文单词，用百度查中文单词的意思，如果查到的中文单词的意思和英语国语字典或者用GOOGLE英文网页的英文单词的解释是一致，翻译软件翻译的是正确的，如果不一致，翻译软件翻译的是错的！翻译工具：1) 在线词典有道词典：/爱词霸：/responsibly/用爱词霸的词典选项。
注：以上两个在线词典的准确率相对较高，可用于查词组译法，灵格斯字典，360上下载，到软件管家里找就可以，这里技术类名词多一些。
爱词霸英中互译翻译软件：/尽量不使用GOOGLE翻译软件。
可参考：GOOGLE翻译软件: /#ja|zh-CN|%0D%0A GOOGLE翻译软件总有翻译错的时候，所以，用GOOGLE翻译软件要注意！牛津字典，或者英语国语词典，灵格斯字典下载一个，自己下载一个吧！注意！！必须有英语国语词典，网上的！。
SQL Server数据库管理外文翻译文献
SQL Server数据库管理外文翻译文献本文翻译了一篇关于SQL Server数据库管理的外文文献。
摘要该文献介绍了SQL Server数据库管理的基本原则和策略。
作者指出，重要的决策应该基于独立思考，避免过多依赖外部帮助。
对于非可确认的内容，不应进行引用。
文献还强调了以简单策略为主、避免法律复杂性的重要性。
内容概述本文详细介绍了SQL Server数据库管理的基本原则和策略。
其中包括：1. 独立决策：在数据库管理中，决策应该基于独立思考。
不过多依赖用户的帮助或指示，而是依靠数据库管理员的专业知识和经验进行决策。
独立决策：在数据库管理中，决策应该基于独立思考。
不过多依赖用户的帮助或指示，而是依靠数据库管理员的专业知识和经验进行决策。
2. 简单策略：为了避免法律复杂性和错误的决策，应采用简单策略。
这意味着避免引用无法确认的内容，只使用可靠和可验证的信息。
简单策略：为了避免法律复杂性和错误的决策，应采用简单策略。
这意味着避免引用无法确认的内容，只使用可靠和可验证的信息。
3. 数据库管理准则：文献提出了一些SQL Server数据库管理的准则，包括：规划和设计数据库结构、有效的数据备份和恢复策略、用户权限管理、性能优化等。
数据库管理准则：文献提出了一些SQL Server数据库管理的准则，包括：规划和设计数据库结构、有效的数据备份和恢复策略、用户权限管理、性能优化等。
结论文献通过介绍SQL Server数据库管理的基本原则和策略，强调了独立决策和简单策略的重要性。
数据库管理员应该依靠自己的知识和经验，避免过度依赖外部帮助，并采取简单策略来管理数据库。
此外，遵循数据库管理准则也是确保数据库安全和性能的重要手段。
以上是对于《SQL Server数据库管理外文翻译文献》的详细内容概述和总结。
如果需要更多详细信息，请阅读原文献。
数据库外文参考文献及翻译
数据库外文参考文献及翻译数据库外文参考文献及翻译SQL ALL-IN-ONE DESK REFERENCE FOR DUMMIESData Files and DatabasesI. Irreducible complexityAny software system that performs a useful function is going to be complex. The more valuable the function, the more complex its implementation will be. Regardless of how the data is stored, the complexity remains. The only question is where that complexity resides. Any non-trivial computer application has two major components: the program the data. Although an application’s level of complexity depends on the task to be performed, developers have some control over the location of that complexity. The complexity may reside primarily in the program part of the overall system, or it may reside in the data part.Operations on the data can be fast. Because the programinteracts directly with the data, with no DBMS in the middle, well-designed applications can run as fast as the hardware permits. What could be better? A data organization that minimizes storage requirements and at the same time maximizes speed of operation seems like the best of all possible worlds. But wait a minute . Flat file systems came into use in the 1940s. We have known about them for a long time, and yet today they have been almost entirely replaced by database s ystems. What’s up with that? Perhaps it is the not-so-beneficial consequences。
大数据挖掘外文翻译文献
文献信息：文献标题：A Study of Data Mining with Big Data（大数据挖掘研究）国外作者：VH Shastri，V Sreeprada文献出处：《International Journal of Emerging Trends and Technology in Computer Science》,2016,38(2):99-103字数统计：英文2291单词，12196字符；中文3868汉字外文文献：A Study of Data Mining with Big DataAbstract Data has become an important part of every economy, industry, organization, business, function and individual. Big Data is a term used to identify large data sets typically whose size is larger than the typical data base. Big data introduces unique computational and statistical challenges. Big Data are at present expanding in most of the domains of engineering and science. Data mining helps to extract useful data from the huge data sets due to its volume, variability and velocity. This article presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective.Keywords: Big Data, Data Mining, HACE theorem, structured and unstructured.I.IntroductionBig Data refers to enormous amount of structured data and unstructured data thatoverflow the organization. If this data is properly used, it can lead to meaningful information. Big data includes a large number of data which requires a lot of processing in real time. It provides a room to discover new values, to understand in-depth knowledge from hidden values and provide a space to manage the data effectively. A database is an organized collection of logically related data which can be easily managed, updated and accessed. Data mining is a process discovering interesting knowledge such as associations, patterns, changes, anomalies and significant structures from large amount of data stored in the databases or other repositories.Big Data includes 3 V’s as its characteristics. They are volume, velocity and variety. V olume means the amount of data generated every second. The data is in state of rest. It is also known for its scale characteristics. Velocity is the speed with which the data is generated. It should have high speed data. The data generated from social media is an example. Variety means different types of data can be taken such as audio, video or documents. It can be numerals, images, time series, arrays etc.Data Mining analyses the data from different perspectives and summarizing it into useful information that can be used for business solutions and predicting the future trends. Data mining (DM), also called Knowledge Discovery in Databases (KDD) or Knowledge Discovery and Data Mining, is the process of searching large volumes of data automatically for patterns such as association rules. It applies many computational techniques from statistics, information retrieval, machine learning and pattern recognition. Data mining extract only required patterns from the database in a short time span. Based on the type of patterns to be mined, data mining tasks can be classified into summarization, classification, clustering, association and trends analysis.Big Data is expanding in all domains including science and engineering fields including physical, biological and biomedical sciences.II.BIG DATA with DATA MININGGenerally big data refers to a collection of large volumes of data and these data are generated from various sources like internet, social-media, business organization, sensors etc. We can extract some useful information with the help of Data Mining. It is a technique for discovering patterns as well as descriptive, understandable, models from a large scale of data.V olume is the size of the data which is larger than petabytes and terabytes. The scale and rise of size makes it difficult to store and analyse using traditional tools. Big Data should be used to mine large amounts of data within the predefined period of time. Traditional database systems were designed to address small amounts of data which were structured and consistent, whereas Big Data includes wide variety of data such as geospatial data, audio, video, unstructured text and so on.Big Data mining refers to the activity of going through big data sets to look for relevant information. To process large volumes of data from different sources quickly, Hadoop is used. Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. Its distributed supports fast data transfer rates among nodes and allows the system to continue operating uninterrupted at times of node failure. It runs Map Reduce for distributed data processing and is works with structured and unstructured data.III.BIG DATA characteristics- HACE THEOREM.We have large volume of heterogeneous data. There exists a complex relationship among the data. We need to discover useful information from this voluminous data.Let us imagine a scenario in which the blind people are asked to draw elephant. The information collected by each blind people may think the trunk as wall, leg as tree, body as wall and tail as rope. The blind men can exchange information with each other.Figure1: Blind men and the giant elephantSome of the characteristics that include are:i.Vast data with heterogeneous and diverse sources: One of the fundamental characteristics of big data is the large volume of data represented by heterogeneous and diverse dimensions. For example in the biomedical world, a single human being is represented as name, age, gender, family history etc., For X-ray and CT scan images and videos are used. Heterogeneity refers to the different types of representations of same individual and diverse refers to the variety of features to represent single information.ii.Autonomous with distributed and de-centralized control: the sources are autonomous, i.e., automatically generated; it generates information without any centralized control. We can compare it with World Wide Web (WWW) where each server provides a certain amount of information without depending on other servers.plex and evolving relationships: As the size of the data becomes infinitely large, the relationship that exists is also large. In early stages, when data is small, there is no complexity in relationships among the data. Data generated from social media and other sources have complex relationships.IV.TOOLS:OPEN SOURCE REVOLUTIONLarge companies such as Facebook, Yahoo, Twitter, LinkedIn benefit and contribute work on open source projects. In Big Data Mining, there are many open source initiatives. The most popular of them are:Apache Mahout:Scalable machine learning and data mining open source software based mainly in Hadoop. It has implementations of a wide range of machine learning and data mining algorithms: clustering, classification, collaborative filtering and frequent patternmining.R: open source programming language and software environment designed for statistical computing and visualization. R was designed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand beginning in 1993 and is used for statistical analysis of very large data sets.MOA: Stream data mining open source software to perform data mining in real time. It has implementations of classification, regression; clustering and frequent item set mining and frequent graph mining. It started as a project of the Machine Learning group of University of Waikato, New Zealand, famous for the WEKA software. The streams framework provides an environment for defining and running stream processes using simple XML based definitions and is able to use MOA, Android and Storm.SAMOA: It is a new upcoming software project for distributed stream mining that will combine S4 and Storm with MOA.Vow pal Wabbit: open source project started at Yahoo! Research and continuing at Microsoft Research to design a fast, scalable, useful learning algorithm. VW is able to learn from terafeature datasets. It can exceed the throughput of any single machine networkinterface when doing linear learning, via parallel learning.V.DATA MINING for BIG DATAData mining is the process by which data is analysed coming from different sources discovers useful information. Data Mining contains several algorithms which fall into 4 categories. They are:1.Association Rule2.Clustering3.Classification4.RegressionAssociation is used to search relationship between variables. It is applied in searching for frequently visited items. In short it establishes relationship among objects. Clustering discovers groups and structures in the data.Classification deals with associating an unknown structure to a known structure. Regression finds a function to model the data.The different data mining algorithms are:Table 1. Classification of AlgorithmsData Mining algorithms can be converted into big map reduce algorithm based on parallel computing basis.Table 2. Differences between Data Mining and Big DataVI.Challenges in BIG DATAMeeting the challenges with BIG Data is difficult. The volume is increasing every day. The velocity is increasing by the internet connected devices. The variety is also expanding and the organizations’ capability to capture and process the data is limited.The following are the challenges in area of Big Data when it is handled:1.Data capture and storage2.Data transmission3.Data curation4.Data analysis5.Data visualizationAccording to, challenges of big data mining are divided into 3 tiers.The first tier is the setup of data mining algorithms. The second tier includesrmation sharing and Data Privacy.2.Domain and Application Knowledge.The third one includes local learning and model fusion for multiple information sources.3.Mining from sparse, uncertain and incomplete data.4.Mining complex and dynamic data.Figure 2: Phases of Big Data ChallengesGenerally mining of data from different data sources is tedious as size of data is larger. Big data is stored at different places and collecting those data will be a tedious task and applying basic data mining algorithms will be an obstacle for it. Next we need to consider the privacy of data. The third case is mining algorithms. When we are applying data mining algorithms to these subsets of data the result may not be that much accurate.VII.Forecast of the futureThere are some challenges that researchers and practitioners will have to deal during the next years:Analytics Architecture:It is not clear yet how an optimal architecture of analytics systems should be to deal with historic data and with real-time data at the same time. An interesting proposal is the Lambda architecture of Nathan Marz. The Lambda Architecture solves the problem of computing arbitrary functions on arbitrary data in real time by decomposing the problem into three layers: the batch layer, theserving layer, and the speed layer. It combines in the same system Hadoop for the batch layer, and Storm for the speed layer. The properties of the system are: robust and fault tolerant, scalable, general, and extensible, allows ad hoc queries, minimal maintenance, and debuggable.Statistical significance: It is important to achieve significant statistical results, and not be fooled by randomness. As Efron explains in his book about Large Scale Inference, it is easy to go wrong with huge data sets and thousands of questions to answer at once.Distributed mining: Many data mining techniques are not trivial to paralyze. To have distributed versions of some methods, a lot of research is needed with practical and theoretical analysis to provide new methods.Time evolving data: Data may be evolving over time, so it is important that the Big Data mining techniques should be able to adapt and in some cases to detect change first. For example, the data stream mining field has very powerful techniques for this task.Compression: Dealing with Big Data, the quantity of space needed to store it is very relevant. There are two main approaches: compression where we don’t loose anything, or sampling where we choose what is thedata that is more representative. Using compression, we may take more time and less space, so we can consider it as a transformation from time to space. Using sampling, we are loosing information, but the gains inspace may be in orders of magnitude. For example Feldman et al use core sets to reduce the complexity of Big Data problems. Core sets are small sets that provably approximate the original data for a given problem. Using merge- reduce the small sets can then be used for solving hard machine learning problems in parallel.Visualization: A main task of Big Data analysis is how to visualize the results. As the data is so big, it is very difficult to find user-friendly visualizations. New techniques, and frameworks to tell and show stories will be needed, as for examplethe photographs, infographics and essays in the beautiful book ”The Human Face of Big Data”.Hidden Big Data: Large quantities of useful data are getting lost since new data is largely untagged and unstructured data. The 2012 IDC studyon Big Data explains that in 2012, 23% (643 exabytes) of the digital universe would be useful for Big Data if tagged and analyzed. However, currently only 3% of the potentially useful data is tagged, and even less is analyzed.VIII.CONCLUSIONThe amounts of data is growing exponentially due to social networking sites, search and retrieval engines, media sharing sites, stock trading sites, news sources and so on. Big Data is becoming the new area for scientific data research and for business applications.Data mining techniques can be applied on big data to acquire some useful information from large datasets. They can be used together to acquire some useful picture from the data.Big Data analysis tools like Map Reduce over Hadoop and HDFS helps organization.中文译文：大数据挖掘研究摘要数据已经成为各个经济、行业、组织、企业、职能和个人的重要组成部分。
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Transact-SQL Cookbook第一章数据透视表1.1使用数据透视表1.1.1 问题支持一个元素序列往往需要解决各种问题。
例如，给定一个日期范围，你可能希望产生一行在每个日期的范围。
或者，您可能希望将一系列的返回值在单独的行成一系列单独的列值相同的行。
实现这种功能，你可以使用一个永久表中存储一系列的顺序号码。
这种表是称为一个数据透视表。
许多食谱书中使用数据透视表，然后，在所有情况下，表的名称是。
这个食谱告诉你如何创建表。
1.1.2 解决方案首先，创建数据透视表。
下一步，创建一个表名为富，将帮助你在透视表：CREATE TABLE Pivot (i INT,PRIMARY KEY(i))CREATE TABLE Foo(i CHAR(1))富表是一个简单的支持表，你应插入以下10行：INSERT INTO Foo VALUES('0')INSERT INTO Foo VALUES('1')INSERT INTO Foo VALUES('2')INSERT INTO Foo VALUES('3')INSERT INTO Foo VALUES('4')INSERT INTO Foo VALUES('5')INSERT INTO Foo VALUES('6')INSERT INTO Foo VALUES('7')INSERT INTO Foo VALUES('8')INSERT INTO Foo VALUES('9')利用10行在富表，你可以很容易地填充枢轴表1000行。
得到1000行10行，加入富本身三倍，创建一个笛卡尔积：INSERT INTO PivotSELECT f1.i+f2.i+f3.iFROM Foo f1, Foo F2, Foo f3如果你名单上的行数据透视表，你会看到它所需的数目的元素，他们将编号从0到999。
1.1.3讨论你会看到食谱，跟随在这本书中，枢轴表通常是用来添加一个排序属性查询。
某种形式的数据透视表中发现许多数据库为基础的系统，尽管它往往是隐藏的用户，主要用在预定义的查询和程序。
你已经看到一些表连接（的富表）控制的行数，我们插入语句生成的数据透视表。
从0到999的值是通过连接生成的字符串。
数字值，是字符串。
因此，当加号（+）运算符用来串连，我们得到的结果如下：'0' + '0' + '0' = '000''0' + '0' + '1' = '001这些结果是插入整数列在目的地的数据透视表。
当你使用一个插入语句插入字符串到整数列的数据库，含蓄地转换成整数的字符串。
笛卡尔积富情况下确保所有可能的组合生成，和，因此，所有可能的值从0到999的产生。
这是值得指出的，这个例子使用行从0999和负数。
你可以很容易地产生负面的号码，如果需要，重复插入声明“-”符号前面的连接字符串，小心点大约0排。
有没有这样的事，作为一个- 0，所以你不想将' 000 '行时产生的负轴数。
如果你这样做，你最终会与0行的数据透视表。
在我们的例子中，0行是不可能的，因为我们定义一个主键的透视表。
枢轴表可能是最有用的表中的世界。
一旦你使用它，它几乎是不可能创造一个严重的应用没有它。
作为一个示范，让我们用枢轴表生成一个图表迅速从32码到126：SELECT i Ascii_Code, CHAR(i) Ascii_Char FROM PivotWHERE i BETWEEN 32 AND 126Ascii_CodeAscii_Char----------- ----------3233 !34 "35 #36 $37 %38 &39 '40 (41 )42 *43 +44 ,45 -46 .47 /48 049 150 251 3...如何更好的使用数据透视表在这个特定的例子是你产生行输出不具有同等数量的行输入。
没有数据透视表，这是困难的，如果不是不可能的任务。
简单的指定一个范围，然后选择枢轴行在该范围内，我们能够产生的数据，不存在任何数据库中的表。
作为另一个例子，数据透视表的有用性，我们可以很容易地使用它来生成一个日历的下一个七天：SELECTCONVERT(CHAR(10),DATEADD(d,i,CURRENT_TIMESTAMP), 121) date, DATENAME(dw,DATEADD(d,i,CURRENT_TIMESTAMP)) day FROM PivotWHERE i BETWEEN 0 AND 6date day---------- ------------------------------2001-11-05 Monday2001-11-06 Tuesday2001-11-07 Wednesday2001-11-08 Thursday2001-11-09 Friday2001-11-10 Saturday2001-11-11 Sunday这些查询只是快速震荡，列在这里向您展示如何一个数据透视表可用于查询。
你会看到其他的食谱，枢轴表往往是一个必不可少的工具，为快速有效的解决问题。
第二章集结构化查询语言，作为一种语言，是围绕这一概念集。
你可能记得在小学学习，或者也许你研究套代数在高中或大学。
虽然语句如选择，更新，删除和可用于在一个数据行在一个时间，该报表设计运行数据集，且你获得最好的优势时，使用这种方式。
尽管这一切，我们通常看到的程序，使用操纵数据一次一行，而不是采取优势的强大的订珠加工能力。
我们希望，这一章，我们可以打开你的眼睛的力量，集合操作。
当你写语句，不知道对程序，选择一个记录，更新它，然后选择另一个。
相反，认为无论在经营上的记录集，一下子。
如果你使用的程序性思维，思维可以采取一些习惯。
为了帮助你，这一章提出了一些食谱表明权力的一套面向编程方法与结构化查询语言。
食谱，在本章的组织表现出不同类型的操作，可以进行设置。
你会看到如何找到共同的要素，总结的一组数据，并找出元素集是一个极端。
行动不一定符合数学定义的集合运算。
相反，我们这些定义和解决现实世界的问题，用代数术语。
在现实世界中，有些偏离严格的数学定义是必要的。
例如，它往往是必要的元素的集合，一个操作是不可能的数学定义集。
2.1简介潜水前的食谱，我们想通过一些基本步骤作了简要的概念和定义的术语在本章。
虽然我们相信你所熟悉的数学概念，交叉口，和工会，我们想把这些set-algebra条款纳入一个现实世界的例子。
2.1.1部件有三种类型的部件时应注意工作组。
第一个是自己设定的。
一个集合是一个集合的元素，和，为我们的宗旨，元素是数据库表中的行或列的查询返回的。
最后，我们的宇宙，这是我们长期使用参考的所有可能的元素为一组给定。
2.1.1.1集一个集合是一个集合的元素。
根据定义，内容不得复制，和他们没有命令。
在这里，数学定义的一组不同于其实际使用中的语言。
在现实世界中，它往往是有益的排序集合的元素到一个指定的顺序。
这样做可以让你找到极端等五大，或底部五，记录。
图2 - 1显示了一例2套。
我们会提到这些例子，我们讨论的各个方面的术语。
我们的目的，我们将考虑一组是一个收集表中的行确定一个共同的元素。
考虑，例如，下面的表项。
这张桌子是一家集集，其中每个集是一个独特的标识order-identification数。
CREATE TABLE OrderItems(OrderId INTEGER,ItemId INTEGER,ProductIdCHAR(10),Qty INTEGER,PRIMARY KEY(OrderId,ItemId))每一集都在这个案件是一个秩序和有很多元素，不重复。
将元素行定义产品的数量和这些产品被命令。
常见的元素是订单列。
使用SQL，很容易从一组列表中的所有元素。
你只是问题的一条语句的选择与确定一套具体的利益。
以下查询将返回所有单项记录集合中的顺序确定的：SELECT * FROM OrderItems WHERE OrderId=112在这一章中，我们将与集，总是在一个表。
许多作者试图证明集合操作使用不同的表。
这个方法有2个问题。
首先，从实证角度而有利，你很少会发现一个数据库表，都具有相同的结构。
其次，有许多隐藏的可能性书面查询来当你认为不同的设置为不同的片同表。
通过集中在一个表，我们希望能打开你的心，这些可能性。
2.1.1.2元素一个元素是一个成员的一组。
图2 - 1，每一个人的信是一个元素。
我们的目的，工作时，一个元素是一个行的表。
结构化查询语言，它往往是有益的，不认为元素统一实体。
在纯数学意义上来说，这是不可能的，一个集合的元素划分为2个或多个组件。
结构化查询语言，然而，你可以分为组成元素。
一个表通常是由许多不同的栏目，你就会经常查询写入操作只有一个子集，这些列。
例如，让我们说，你想找到的所有订单，包含一个炸药，无论数量。
你的元素排在orderitems表。
你需要使用产品编号列识别爆炸物，你会需要返回订单列确定的订单，但你没有使用其他表中的列。
这里的查询：SELECT OrderIdFROM OrderItems oGROUP BY OrderIdHAVING EXISTS(SELECT *FROM OrderItems o1WHERE o1.ProductId='Explosive' AND o.OrderId=o1.OrderId) 此查询实际使用的一组操作，你会读到这一章。
操作称为包含操作，和它对应的查询关键字的存在。
2.1.1.3合集一个合集的所有可能的元素可以是一个给定的集合。
考虑1和2图。
每一集是由字母的字母表。
如果我们决定，只有字母可以设置元素，宇宙的两队会设置的所有信件，如图2 - 2所示。
一个更现实的例子一个宇宙，认为一个学校课程的学生提供40种可能。
每个学生选择一个小数目40课程采取在某一学期。
课程内容。
本课程，使学生正在制定一套。
不同的学生采取不同的组合和数量的课程。
该集是不一样的，也不是所有大小相同，但他们都包含元素相同的宇宙。
每个学生必须选择从相同的40种可能性。
在学生/课程的例子了，所有的元素都来自同一个宇宙。
它也可能为一些套在一个表有不同的宇宙人。
例如，假设一个表列出完成案例研究，学生提出了。
进一步假设，宇宙可能的情况是不同的每个过程研究。
如果你认为一套定义一个课程和学生，宇宙的元素，将取决于课程的学生。