数据库设计外文翻译
数据库(中英文翻译)
原文：Planning the DatabaseIt is important to plan how the logical storage structure of the database will affect system performance and various database management operations. For example, before creating any tablespaces for your database, you should know how many data files will make up the tablespace,what type of information will be stored in each tablespace, and on which disk drives the datafiles will be physically stored. When planning the overall logical storage of the database structure, take into account the effects that this structure will have when the database is actually created and running.You may have database objects that have special storage requirements dueto type or size.In distributed database environments, this planning stage is extremely important. The physical location of frequently accessed data dramatically affects application performance.During the planning stage, develop a backup strategy for the database. You can alter the logical storage structure or design of the database to improve backup efficiency. Backup strategies are introduced in a later lesson.These are the types of questions and considerations, which you will encounter as a DBA, and this course (in its entirety) is designed to help you answer them.Databases: ExamplesDifferent types of databases have their own specific instance and storage requirements. YourOracle database software includes templates for the creation of these different types of databases.Characteristics of these examples are the following:• Data Warehouse: Store data for long periods and retrieve them in read operations.• Transaction Processing: Accommodate many, but usually small, transactions.• General Purpose: Work with transactions and store them for a medium length of time.Database Configuration Assistant (DBCA)You can use the Database Configuration Assistant (DBCA) to create, change the configuration of, or delete a database. You can also create a database from a list of predefined templates or use an existing database as a sample to create a new database or template. This is sometimes referred to as “database cloning.”You can invoke the DBCA by performing the following steps:1. Log on to your computer as a member of the administrative group that is authorized to install the Oracle software.2. If required, set environment variables.3. Enter dbca to invoke the DBCA.4. Click Next to continue.DBCA offers you a choice of assisting with several operations, for example, creating a database.Using the DBCA to Create a DatabaseYou can use the DBCA to create a database as follows:1. Select Create a Database on the DBCA Operations page to invoke a wizard that enables you to configure and create a database.The wizard prompts you to provide configuration information as outlined in the steps that follow. On most pages, the wizard provides a default setting that you can accept.2. Select the type of database template to be used in creating the database. There aretemplates for Data Warehouse, General Purpose, and Transaction Processing databases that copy a preconfigured database, including data files. These data files include control files,redo log files, and data files for various included tablespaces.Click Show Details to see the configuration for each type of database.For more complex environments, you may want to select the Custom Database option. Password ManagementAfter the DBCA finishes, note the following information for future reference:• Location of installation log files (see A)• Global database name (see B)• System identifier (SID) (see B)• Server parameter file name and location (see B)• Enterpr ise Manager URL (see C)Click Password Management to unlock database accounts that you plan to use.Provide apassword when you unlock an account.Creating a Database Design TemplateA template is a predefined database definition that you use as a starting point for a new database.If you do not create a template as part of the database creation process, you can do it anytime by invoking the DBCA. You have three ways to create a template: • From an existing template• From an existing database (structure only)• From an existing database (structure as well as data)The DBCA guides you through the steps to create a database design template.Using the DBCA to Delete a DatabaseTo delete (or configure) a database in UNIX or Linux, you must set ORACLE_SID in the shell from which DBCA is launched. Start the DBCA by entering dbca in a terminal window, and click Next on the Welcome page. To delete the database, perform the following steps:1. On the Operations page, select Delete a Database, and click Next.2. Select the database that you want to delete (in class, hist), and click Finish.3. Click Yes to confirm your deletion.Using the DBCA to Delete a Database (continued)Dropping a database involves removing its data files, redo log files, control files, and initialization parameter files. The DROP DATABASE statement deletes all control files and all other database files listed in the control file. To use the DROP DATABASE statement successfully,all the following conditions must apply:The database must be mounted and closed.The database must be mounted exclusively—not in shared mode.The database must be mounted as RESTRICTED.An example of this statement is:DROP DATABASE;The DROP DATABASE statement has no effect on archived log files nor does it have any effect on copies or backups of the database. It is best to use Recovery Manager (RMAN) to delete such files. If the database is on raw disks, then the actual raw disk special files are not deleted.Management FrameworkThere are three major components of the Oracle database management framework: • The database instance that is being managed• A listener that allows connections to the database• The management interface. This may be either a management agent running onthe database server (which connects it to Oracle Enterprise Manager Grid Control) or the stand-alone Oracle Enterprise Manager Database Control. This is also referred to as Database Console.Each of these components must be explicitly started before you can use the services of the component and must be shut down cleanly when shutting down the server hosting the Oracle database.The first component to be started is the management interface. After this is activated, the management interface can be used to start the other components. Starting and Stopping Database ControlOracle provides a stand-alone management console called Database Control for databases that are not connected to the Grid Control framework. Each database that is managed with Database Control has a separate Database Control installation, and from any one Database Control, you can manage only one database. Before using Database Control, ensure that a dbconsole process is started.To start the dbconsole process, use the following command:emctl start dbconsole To stop the dbconsole process, use the following command:emctl stop dbconsole To view the status of the dbconsole process, use the following command:emctl status dbconsole.Note: You may need to navigate to your $ORACLE_HOME/bin directory if this directory is not in your operating system (OS) path.Database Control uses a server-side agent process. This agent process automatically starts and stops when the dbconsole process is started or stopped.译文：规划数据库规划如何对数据库的逻辑存储结构将影响系统的性能和各种数据库管理操作是非常重要的。
数据库毕业设计---外文翻译
附录附录A: 外文资料翻译－原文部分：CUSTOMER TARGETTINGThe earliest determinant of success in the development of a profitable card scheme will lie in the quality of applicants that are attracted by the marketing effort. Not only must there be sufficient creditworthy applicants to avoid fruitless and expensive application processing, but it is critical that the overall mix of new accounts meets the standard necessary to ensure ultimate profitability. For example, the marketing initiatives may attract sufficient volume of applicants that are assessed as above the scorecard cut-off, but the proportion of acceptances in the upper bands may be insufficient to deliver the level of profit and lesser bad debt required to achieve the financial objectives of the scheme.This chapter considers the range of data sources available to support the development of a credit card scheme and the tools that can be applied to maximize the flow of applications from the required categories.Data availabilityThe data that makes up the ingredients from which marketing campaigns can be constructed can come from many diverse sources. Typically, it will fall into four categories:1 the national or regional register of voters;2 the national or regional register of court judgments that records the outcomeof creditor-debtor legislation;3 any national or regional pooled information showing the credit history of clients of the participating lenders; and4 commercially compiled data including and culled from name and address lists, survey results and other market analysis data, e.g. neighborhoods and lifestyle categorization through geo-demographic information systems.The availability and quality of this data will vary from country to country and bureau to bureau.Availability is not only governed by the extent to which the responsible agency has undertaken to record it, but also by the feasibility of accessing the data and the extent (if any) to which local consumer legislation or other considerations (e.g. religious principles) will allow it to be used. Other limitations on the use of available data may lie in the simple impossibility or expense of accessing the information sources, perhaps because necessary consumer consent for divulgence has been withheld or because the records are not yet stored electronically.The local credit information bureaux will be able to provide guidance on all of these matters, as will many local trade or professional associations or the relevant government departments.Data segmentation and AnalysesThe following remarks deal with the ways in which lawfully obtained data may then be processed and analyzed in order to maximize its value as the basis of a marketing prospect list. Examples of the types and uses of data that will play a role in the credit decision area are discussed later in the chapter, within the context of application processing.The key categories into which prospects may be segmented include lifestyle, propensity to purchase specific products (financial or otherwise) and levels of risk. The leading international information bureaux will be able to provide segmentation systems that are able to correlate each of these data categories to provide meaningful prospect lists in rank order. Additionally, many bureaux will have the capability to further enhance the strength and value of the data. Through the selective purchasing of data from bona fide market sources, and by overlaying generic factors deduced from the analysis of the broad mass of industry information that routinely passes through their systems, the best international operators are now able to offer marketing and credit information support that can add significantly to the quality of new applicants.The importance of the role and standard of this data in influencing the quality of the target population for mailings, etc. should not be underestimated. Information that is dated or inaccurate may not only lead a marketer and the organization into embarrassment and damage their reputations, but it will also open the credit card scheme to applicants from outside either the target sector or ,worse still, applicants outside the lender’s view of an acceptable credit risk.From this, it follows that you should seek to use an information bureau whose business principles and operating practices comply with the highest levels of both competence and integrity.Developing the prospect databaseThis is the process by which the raw data streams are brought together and subjected to progressive refinement, with the output representing the refined base from which prospecting can begin in earnest. A wide experience-often across many different markets and countries-in the sourcing, handling and analysis of data inevitably improves the quality of the ideas and systems that a bureau can offer for the development of the prospect database.In summary, the typical shape of the service available from the very best bureaux will support a process that runs as follows:1.collect and consolidate all data to be screened for inclusion;2.merge the various streams;3.sort and classify the data by market and credit categories;4.screen the date using predetermined marketing and credit criteria; and5.consolidate and output the refined list.Bureaux will charge for the use of their expertise and systems.Therefore, consideration should be given to the volumes of data that are to be processed and the costs involved at each stage. The most cost-effective approach to constructing prospect databases only undertakes the lowest-cost screening process within the earlier stages. The more expensive screening processes are not employed until the mass of the data has been reduced by earlier filtering.It is impossible to be prescriptive about the range and levels of service that are available, but reference to one of the major bureaux operating in the region could certainly be a good starting point.Campaign Management and AnalysisAgain, this is an area where excellent support is available from the best-of-breed bureaux. They will provide both the operational support and software capabilities to mount, monitor and analyse your marketing campaign, should you so wish. Their depth of experience and capabilities in the credit sector will often open up income: cost possibilities from the solicitation exercise that would not otherwise be available to the new entrant.The First Important Applications of DBMS’sData items include names and addresses of customers, accounts, loans and their balance, and the connection between customers and their accounts and loans, e.g., who has signature authority over which accounts. Queries for account balances are common, but far more common are modifications representing a single payment from or deposit to an account.As with the airline reservation system, we expect that many tellers and customers (through ATM machines) will be querying and modifying the bank’s data at once. It is vital that simultaneous accesses to an account not cause the effect of an ATM transaction to be lost. Failures cannot be tolerated. For example, once the money has been ejected from an ATM machine ,the bank must record the debit, even if the power immediately fails. On the other hand, it is not permissible for the bank to record the debit and then not deliver the money because the power fails. The proper way to handle this operation is far from obvious and can be regarded as one of the significant achievements in DBMS architecture.Database system changed significantly. Codd proposed that database system should present the user with a view of data organized as tables called relations. Behindthe scenes, there might be a complex data structure that allowed rapid response to a variety of queries. But unlike the user of earlier database systems, the user of a relational system would not be concerned with storage structure. Queries could be expressed in a very high level language, which greatly increased the efficiency of database programmers. Relations are tables. Their columns are headed by attributes.Client –Server ArchitectureMany varieties of modern software use a client-server architecture, in which requests by one process (the client ) are sent to another process (the server) for execution. Database systems are no exception, and it is common to divide the work of the components shown into a server process and one or more client processes.In the simplest client/server architecture, the entire DBMS is a server, except for the query interfaces that the user and send queries or other commands across to the server. For example, relational systems generally use the SQL language for representing requests from the client to the server. The database server then sends the answer, in the form of a table or relation, back to client. The relationship between client and server can get more complex, especially when answers are extremely large. We shall have more to say about this matter in section 1.3.3. there is also a trend to put more work in the client, since the server will be a bottleneck if there are many simultaneous database users.附录B: 外文资料翻译－译文部分：客户目标：最早判断发展可收益卡的成功性是在于受市场影响的被吸引的申请人的质量。
大学毕业设计关于数据库外文翻译2篇
原文：Structure of the Relational database—《Database System Concepts》Part1: Relational Databases The relational model is the basis for any relational database management system (RDBMS).A relational model has three core components: a collection of obj ects or relations, operators that act on the objects or relations, and data integrity methods. In other words, it has a place to store the data, a way to create and retrieve the data, and a way to make sure that the data is logically consistent.A relational database uses relations, or two-dimensional tables, to store the information needed to support a business. Let's go over the basic components of a traditional relational database system and look at how a relational database is designed. Once you have a solid understanding of what rows, columns, tables, and relationships are, you'll be well on your way to leveraging the power of a relational database.Tables, Row, and ColumnsA table in a relational database, alternatively known as a relation, is a two-dimensional structure used to hold related information. A database consists of one or more related tables.Note: Don't confuse a relation with relationships. A relation is essentially a table, and a relationship is a way to correlate, join, or associate two tables.A row in a table is a collection or instance of one thing, such as one employee or one line item on an invoice. A column contains all the information of a single type, and the piece of data at the intersection of a row and a column, a field, is the smallest piece of information that can be retrieved with the database's query language. For example, a table with information about employees might have a column calledLAST_NAME that contains all of the employees' last names. Data is retrieved from a table by filtering on both the row and the column.Primary Keys, Datatypes, and Foreign KeysThe examples throughout this article will focus on the hypothetical work of Scott Smith, database developer and entrepreneur. He just started a new widget company and wants to implement a few of the basic business functions using the relational database to manage his Human Resources (HR) department.Relation: A two-dimensional structure used to hold related information, also known as a table.Note: Most of Scott's employees were hired away from one of his previous employers, some of whom have over 20 years of experience in the field. As a hiring incentive, Scott has agreed to keep the new employees' original hire date in the new database.Row:A group of one or more data elements in a database table that describes a person, place, or thing.Column:The component of a database table that contains all of the data of the same name and type across all rows.You'll learn about database design in the following sections, but let's assume for the moment that the majority of the database design is completed and some tables need to be implemented. Scott creates the EMP table to hold the basic employee information, and it looks something like this:Notice that some fields in the Commission (COMM) and Manager (MGR) columns do not contain a value; they are blank. A relational database can enforce the rule that fields in a column may or may not be empty. In this case, it makes sense for an employee who is not in the Sales department to have a blank Commission field. It also makes sense for the president of the company to have a blank Manager field, since that employee doesn't report to anyone.Field:The smallest piece of information that can be retrieved by the database query language. A field is found at the intersection of a row and a column in a database table.On the other hand, none of the fields in the Employee Number (EMPNO) column are blank. The company always wants to assign an employee number to an employee, and that number must be different for each employee. One of the features of a relational database is that it can ensure that a value is entered into this column and that it is unique. Th e EMPNO column, in this case, is the primary key of the table.Primary Key:A column (or columns) in a table that makes the row in the table distinguishable from every other row in the same table.Notice the different datatypes that are stored in the EMP ta ble: numeric values, character or alphabetic values, and date values.As you might suspect, the DEPTNO column contains the department number for the employee. But how do you know what department name is associated with what number? Scott created the DEPT table to hold the descriptions for the department codes in the EMP table.The DEPTNO column in the EMP table contains the same values as the DEPTNO column in the DEPT table. In this case, the DEPTNO column in the EMP table is considered a foreign key to the same column in the DEPT table.A foreign key enforces the concept of referential integrity in a relational database. The concept of referential integrity not only prevents an invalid department number from being inserted into the EMP table, but it also prevents a row in the DEPT table from being deleted if there are employees still assigned to that department.Foreign Key:A column (or columns) in a table that draws its values from a primary or unique key column in another table. A foreign key assists in ensuring the data integrity of a table. Referential Integrity A method employed by a relational database system that enforces one-to-many relationships between tables.Data ModelingBefore Scott created the actual tables in the database, he went through a design process known as data modeling. In this process, the developer conceptualizes and documents all the tables for the database. One of the common methods for mod eling a database is called ERA, which stands for entities, relationships, and attributes. The database designer uses an application that can maintain entities, their attributes, and their relationships. In general, an entity corresponds to a table in the database, and the attributes of the entity correspond to columns of the table.Data Modeling:A process of defining the entities, attributes, and relationships between the entities in preparation for creating the physical database.The data-modeling process involves defining the entities, defining the relationships between those entities, and then defining the attributes for each of the entities. Once a cycle is complete, it is repeated as many times as necessary to ensure that the designer is capturing what is important enough to go into the database. Let's take a closer look at each step in the data-modeling process.Defining the EntitiesFirst, the designer identifies all of the entities within the scope of the database application.The entities are the pers ons, places, or things that are important to the organization and need to be tracked in the database. Entities will most likely translate neatly to database tables. For example, for the first version of Scott's widget company database, he identifies four entities: employees, departments, salary grades, and bonuses. These will become the EMP, DEPT, SALGRADE, and BONUS tables.Defining the Relationships Between EntitiesOnce the entities are defined, the designer can proceed with defining how each of the entities is related. Often, the designer will pair each entity with every other entity and ask, "Is there a relationship between these two entities?" Some relationships are obvious; some are not.In the widget company database, there is most likely a relations hip between EMP and DEPT, but depending on the business rules, it is unlikely that the DEPT and SALGRADE entities are related. If the business rules were to restrict certain salary grades to certain departments, there would most likely be a new entity that defines the relationship between salary grades and departments. This entity wouldbe known as an associative or intersection table and would contain the valid combinations of salary grades and departments.Associative Table:A database table that stores th e valid combinations of rows from two other tables and usually enforces a business rule. An associative table resolves a many-to-many relationship.In general, there are three types of relationships in a relational database:One-to-many The most common type of relationship is one-to-many. This means that for each occurrence in a given entity, the parent entity, there may be one or more occurrences in a second entity, the child entity, to which it is related. For example, in the widget company database, the DEPT entity is a parent entity, and for each department, there could be one or more employees associated with that department. The relationship between DEPT and EMP is one-to-many.One-to-one In a one-to-one relationship, a row in a table is related to only one or none of the rows in a second table. This relationship type is often used for subtyping. For example, an EMPLOYEE table may hold the information common to all employees, while the FULLTIME, PARTTIME, and CONTRACTOR tables hold information unique to full-time employees, part-time employees, and contractors, respectively. These entities would be considered subtypes of an EMPLOYEE and maintain a one-to-one relationship with the EMPLOYEE table. These relationships are not as common as one-to-many relationships, because if one entity has an occurrence for a corresponding row in another entity, in most cases, the attributes from both entities should be in a single entity.Many-to-many In a many-to-many relationship, one row of a table may be related to man y rows of another table, and vice versa. Usually, when this relationship is implemented in the database, a third entity isdefined as an intersection table to contain the associations between the two entities in the relationship. For example, in a database used for school class enrollment, the STUDENT table has a many-to-many relationship with the CLASS table—one student may take one or more classes, and a given class may have one or more students. The intersection table STUDENT_CLASS would contain the comb inations of STUDENT and CLASS to track which students are in which classes.Once the designer has defined the entity relationships, the next step is to assign the attributes to each entity. This is physically implemented using columns, as shown here for th e SALGRADE table as derived from the salary grade entity.After the entities, relationships, and attributes have been defined, the designer may iterate the data modeling many more times. When reviewing relationships, new entities may be discovered. For exa mple, when discussing the widget inventory table and its relationship to a customer order, the need for a shipping restrictions table may arise.Once the design process is complete, the physical database tables may be created. Logical database design sessions should not involve physical implementation issues, but once the design has gone through an iteration or two, it's the DBA's job to bring the designers "down to earth." As a result, the design may need to be revisited to balance the ideal database implementation versus the realities of budgets andschedules.译文：关系数据库的结构—《数据库系统结构》第一章：关系数据库关系模型是任何关系数据库管理系统（RDBMS）的基础。
数据库毕业设计外文翻译--图像系统简介
附录1 外文原文COLOR SYSTEM OVERVIEWIn the age of office automation and electronic imaging, office documents are being processed, transported, and displayed in a variety of ways. The scope of document processing is enormous; it encompasses page layout, document length, collation, simplex/duplex, color, image quality, finishing, and binding. If the office system is networked, then another dimension of network-related issues-protocol, file format, page description language, compression/decompression, job management, error handling, user interface, and device driver-has to be addressed. Digital color-imaging systems process electronic information from various sources; images may come from a local-area network, a remote-sensing device, different color workstations, or a local scanner. After processing, a document is usually compressed and transmitted to several places via a computer network for viewing, editing, or printing. Moreover, the trend in the industry is moving toward an open environment. This means that various devices such as scanners, computers, workstations, modems, and printers from multiple vendors are assembled into one system. Implementations should be based on public-domain technology rather than proprietary standards. This will allow vendors equal access to the market for system components and give users the widest choice in selecting components. It is a vastly large task to enable the communication of all system components regardless of differences in the operating system, file format, page description language, and information content. Ideally, the exchange should not cause information loss or alteration. A closer look at a document may reveal that it consists of different types of images, primarily text, graphs, and pictorial images. These all have different image characteristics and representations such as ASCII (American Standard Code for Information Interchange) for text, vector for graphs, and raster for pictorial images. Each type of image and its associated attributes like the font, font size, halftone, gray level, resolution, and color have to be dealt with differently. In such a complex environment, there is no doubt that many compatibility problems occur when an image is acquired, transmitted, displayed, and rendered. ?With the fast development of Internet technology, large volumes of data in the form of electronic documents from the Web. For the purposes of data integration and data exchange, more and moreexisting sources, such as relational databases, support public XML export, and increasing amount of public and private data is described in a semi-structured way. A number of issues need to be addressed when we integrate data from different sources, including heterogeneous and duplicate data, multiple divisions and partners, and changes.? Data heterogeneity results from the use of different information management systems to store data and each system has its own data structure and access methods. Relational database management systems benefit from the universal acceptance of Structured Query Language (SQL) as the primary means of getting answers whilst document and email repositories are generally accessed using text search engines with varying interfaces and capabilities. Because these systems were not designed with interoperability in mind, each must generally be accessed using source-specific applications or application programming interfaces (APIs).? Another difficulty in data integration is data duplication-different systems represent the same piece of data in different ways. For example, customers may be identified by name in one database, but by account number in a second repository, may identify the same customer by email address. Frequently a required piece of information is derived from multiple data points. Data integration is further complicated when customers do business with multiple divisions within a large company, or with other partners. Similarly, answering questions about the state of a company's supply chain requires access to vendor and distributor information sources. Doing business electronically across the firewall gives rise to security and data ownership issues. Finally, data integration has to deal with different types of changes; change in business requirements and strategies, in IT systems, mergers and acquisitions, and new product launches. This demands that a data integration solution be sufficiently flexible and adaptable.One possible solution for the data integration problems mentioned above is to provide an XML Web services break down the barriers between different computing platforms, development environments and communications networks, allowing organizations to work together electronically without the expense and delay of agreeing on semantics, schema, interfaces, and other application integration. XML provides the flexibility for handling data with differing structures. As XML is becoming the principal medium for data exchange over the Web and for information integration in general,increasing amounts of public and private data are described in XML. XML data is usually defined in a tree or graph based, self-describing object instance model (Boncz and Kersten, 1999). However, semi-structured data is incompatible with the flat structure of relational database tables, and so the growth of XML data requires new and complex query optimization techniques.Creating XML files with a text editor would be a lot easier if you didn't have to close all those HTML tags. First you have to add the XML declaration and the root opening and closing HTML tags. Next, you start adding element opening and closing tags one at a time. Of course, once you have the initial sequence completed you can just copy and paste to repeat the required elements. After doing this hundreds of times you'll be looking for a faster way to create XML files.Some XML editors will automatically add the closing tag after you have finished typing the opening tag but, you still have to type the brackets around the opening tag. I kept thinking this process should be easier. So, I came up with a solution that allows you to create XML files without using HTML tags.This console application will create an XML file based on user input. Just enter the file name, how many element fields you want, and the name of each field. Optionally, you can include a data type separated by a comma after the field name. You can just enter the field name because the data type is not required. The structure of the XML file that is created will be compatible with the .NET Dataset and can be easily added to a database.In addition to creating the XML file, an XSL file and HTML file are also created. The HTML file uses client side JavaScript to transform the XML file using the XSL file. This provides an easy way to view the new XML file by displaying it in a table layout.The download includes both the source code and the already compiled application. You can start using the executable right away or customize it to meet your needs. All you will need is the .NET Framework and a text editor, like Notepad, to build this application.Improving ASP Performance with Data CachingOne of the nicest features of is the ability to cache page content. This can be used to substantially reduce load on a website's database - which is an obvious attraction if the site uses Microsoft's Access to store data rather than SQL Server. Unfortunately there is no built in cachingsystem in classic ASP, but it is easy to build one by using the Application object to store data.When to use ASP Caching. Caching is most useful for data that changes - but not too often. For example an e-commerce store could display a list of popular products, or an information site could display a list of press releases.Don't forget that it is also possible to build functionality into the admin part of the site so that the cache would be flushed if new content is added to the database. That way the website administrator would not have to wait until the cache timed out in order for new content to appear on the website. Remember that data stored in Application variables is visible by all the users of the website。
数据库设计外文翻译
英文摘要Data Transformation ServicesDTS facilitates the import, export, and transformation of heterogeneous data. It supports transformations between source and target data using an OLE DB-based architecture. This allows you to move and transform data between the following data sources:∙Native OLE DB providers such as SQL Server, Microsoft Excel, Microsoft Works, Microsoft Access, and Oracle.∙ODBC data sources such as Sybase and Informix using the OLE DB Provider for ODBC.∙ASCII fixed-field length text files and ASCII delimited text files.For example, consider a training company with four regional offices, each responsible for a predefined geographical region. The company is using a central SQL Server to store sales data. At the beginning of each quarter, each regional manager populates an Excel spreadsheet with sales targets for each salesperson. These spreadsheets are imported to the central database using the DTS Import Wizard. At the end of each quarter, the DTS Export Wizard is used to create a regional spreadsheet that contains target versus actual sales figures for each region.DTS also can move data from a variety of data sources into data marts or data warehouses. Currently, data warehouse products are high-end, complex add-ons. As companies move toward more data warehousing and decision processing systems, the low cost and ease of configuration of SQL Server 7.0 will make it an attractive choice. For many, the fact that much of the legacy data to be analyzed may be housed in an Oracle system will focus their attention on finding the most cost-effective way to get at that data. With DTS, moving and massaging the data from Oracle to SQL Server is less complex and can be completely automated.DTS introduces the concept of a package, which is a series of tasks that are performed as a part of a transformation. DTS has its own in-process component object model (COM) server engine that can be used independent of SQL Server and that supports scripting for each column using Visual Basic® and JScript® development software. Each transformation can include data quality checks and validation, aggregation, and duplicate elimination. You can also combine multiple columns into a single column, or build multiple rows from a single input.Using the DTS Wizard, you can:∙Specify any custom settings used by the OLD DB provider to connect to the data source or destination.∙Copy an entire table, or the results of an SQL query, such as those involving joins of multiple tables or distributed queries. DTS also can copy schema and data between relational databases. However, DTS does not copy indexes, stored procedures, or referential integrity constraints.∙Build a query using the DTS Query Builder Wizard. This allows users inexperienced with the SQL language to build queries interactively.∙Change the name, data type, size, precision, scale, and nullability of a column when copying from the source to the destination, where a valid data-type conversion applies.∙Specify transformation rules that govern how data is copied between columns of different data types, sizes, precisions, scales, and nullabilities.∙Execute an ActiveX script (Visual Basic or JScript) that can modify (transform) the data when copied from the source to the destination. Or you can perform any operation supported by Visual Basic or JScript development software.∙Save the DTS package to the SQL Server MSDB database, Microsoft Repository, or a COM-structured storage file.Schedule the DTS package for later execution.Once the package is executed, DTS checks to see if the destination table already exists, then gives you the option of dropping and recreating the destination table. If the DTS Wizard does not properly create the destination table, verify that the column mappings are correct, select a different data type mapping, or create the table manually and then copy the data.Each database defines its own data types and column and object naming conventions. DTS attempts to define the best possible data-type matches between a source and a destination. However, you can override DTS mappings and specify a different destination data-type, size, precision, and scale properties in the Transform dialog box.Each source and destination may have binary large object (BLOB) limitations. For example, if the destination is ODBC, then a destination table can contain only one BLOB column and it must have a unique index before data can be imported. For more information, see the OLE DB for ODBC driver documentation.Note DTS functionality may be limited by the capabilities of specificdatabase management system (DBMS) or OLE DB drivers.DTS uses the source object’s name as a default. However, you can also add double quote marks (“ “) or square brackets ([ ])around multiword table and column names if this is supported by your DBMS.Data Warehousing and OLAPDTS can function independent of SQL Server and can be used as a stand-alone tool to transfer data from Oracle to any other ODBC- or OLE DB-compliant database. Accordingly, DTS can extract data from operational databases for inclusion in a data warehouse or data mart for query and analysis.Figure 4. DTS and data warehousingIn the previous diagram, the transaction data resides on an IBM DB2 transaction server. A package is created using DTS to transfer and clean the data from the DB2 transaction server and to move it into the data warehouse or data mart. In this example, the relational database server is SQL Server 7.0, and the data warehouse uses OLAP Services to provide analytical capabilities. Client programs (such as Excel) access the OLAP Services server using the OLE DB for OLAP interface, which is exposed through a client-side component called Microsoft PivotTable® Service. Client programs using PivotTable Service can manipulate data in the OLAP server and even change individual cells.SQL Server OLAP Services is a flexible, scalable OLAP solution, providing high-performance access to information in the data warehouse. OLAP Services supports all implementations of OLAP equally well: multidimensional OLAP (MOLAP), relational OLAP (ROLAP), and a hybrid (HOLAP). OLAP Services addresses the most significant challenges in scalability through partial preaggregation, smart client/server caching, virtual cubes, and partitioning.DTS and OLAP Services offer an attractive and cost-effective solution. Data warehousing and OLAP solutions using DTS and OLAP Services are developed with point-and-click graphical tools that are tightly integrated and easy to use. Furthermore, because the PivotTable Service client is using OLE DB, the interface is more open to access by a variety of client applications.Issues for Oracle versions 7.3 and 8.0Oracle does not support more than one BLOB data type per table. This prevents copying SQL Server tables that contain multiple text and image data types with modification. You may want to map one or more BLOBs to the varchar data type and allow truncation, or split a source table into multiple tables. Oracle returns numeric data types such as precision = 38 and scale =0, even when there are digits to the right of the decimal point. If you copy this information, it will be truncated to integer values. If mapped to SQL Server, the precision is reduced to a maximum of 28 digits.The Oracle ODBC driver does not work with DTS and is not supported by Microsoft. Use the Microsoft Oracle ODBC driver that comes with SQL Server. When exporting BLOB data to Oracle using ODBC, the destination table must have an existing unique primary key.Heterogeneous Distributed QueriesDistributed queries access not only data currently stored in SQL Server (homogeneous data), but also access data traditionally stored in a data store other than SQL Server (heterogeneous data). Distributed queries behave as if all data were stored in SQL Server. SQL Server 7.0 will support distributed queries by taking advantage of the UDA architecture (OLE DB) to access heterogeneous data sources, as illustrated in the following diagram.Figure 5. Accessing heterogeneous data sources with UDA翻译DTS 使进口，出口和不同的数据的转变变得容易。
数据库外文翻译毕业设计
Database Management Systems( 3th Edition ),Wiley ,2004,5-12A introduction to Database Management SystemRaghu RamakrishnanA 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 variousdata-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 maybe 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 vagueand/or poorly defined ,but people can “browse”through the database until they have the stored data items and”manage“the needed information. In short, the DBMS willassemble 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 subsystem that 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 usefulnessof database management systems;Managers: who require more up-to-data information to make effective decision Customers: who demand increasingly sophisticated information services andmore current information about the status of their orders, invoices, and accounts. Users: who find that they can develop custom applications with databasesystems 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 whenthe 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 as its 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 onlyspecify 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-liststructure 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 information without 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 s personal'problems of data integrity that we mentioned earlier are solved . But todaycomputers, 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. Adedicated 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 onother 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 datashould 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 .。
数据库毕业设计外文翻译--正确选择数据采集系统
中英文翻译Selecting the Right Data Acquisition SystemEngineers often must monitor a handful of signals over extended periods of time, and then graph and analyze the resulting data. The need to monitor, record and analyze data arises in a wide range of applications, including the design-verification stage of product development, environmental chamber monitoring, component inspection, benchtop testing and process trouble-shooting.This application note describes the various methods and devices you can use to acquire, record and analyze data, from the simple pen-and-paper method to using today's sophisticated data acquisition systems. It discusses the advantages and disadvantages of each method and provides a list of questions that will guide you in selecting the approach that best suits your needs.IntroductionIn geotechnical engineering, we sometime encounter some difficulties such as monitoring instruments distributed in a large area, dangerous environment of working site that cause some difficulty for easy access. In this case, operators may adopt remote control, by which a large amount of measured data will be transmitted to a observation room where the data are to be collected, stored and processed.The automatic data acquisition control system is able to complete the tasks as regular automatic data monitoring, acquisition and store, featuring high automation, large data store capacity and reliable performance.The system is composed of acquisition control system and display system, with the following features:1. No. of Channels: 32 ( can be increased or decreased according to user's real needs.)2. Scanning duration: decided by user, fastest 32 points/second3. Store capacity: 20G( may be increased or decreased)4. Display: (a) Table of parameter (b) History tendency (c) Column graphics.5. Function: real time monitoring control, warning6. Overall dimension: 50cm×50cm×72cmData acquisition systems, as the name implies, are products and/or processes used to collect information to document or analyze some phenomenon. In the simplest form, a technician logging the temperature of an oven on a piece of paper is performing data acquisition. As technology has progressed, this type of process has been simplified and made more accurate, versatile, and reliable through electronic equipment. Equipment ranges from simple recorders to sophisticated computer systems. Data acquisition products serve as a focal point in a system, tying together a wide variety of products, such as sensors that indicate temperature, flow, level, or pressure. Some common data acquistion terms are shown below:Data acquisition technology has taken giant leaps forward over the last 30 to 40 years. For example, 40 years ago, in a typical college lab, apparatus for tracking the temperature rise in a crucible of sodiumtungsten- bronze consisted of a thermocouple, a bridge, a lookup table, a pad of paper and a pencil.Today's college students are much more likely to use an automated process and analyze the data on a PC Today, numerous options are available for gathering data. The optimal choice depends on several factors, including the complexity of the task, the speed and accuracy you require, and the documentation you want. Data acquisition systems range from the simple to the complex, with a range of performance and functionality.Pencil and paperThe old pencil and paper approach is still viable for some situations, and it is inexpensive, readily available, quick and easy to get started. All you need to do is hook up a digital multimeter (DMM) and begin recording data by hand. Unfortunately, this method is error-prone, tends to be slow and requires extensive manual analysis. In addition, it works only for a single channel of data; while you can use multiple DMMs, the system will quickly becomes bulky and awkward. Accuracy is dependent on the transcriber's level of fastidiousness and you may need to scaleinput manually. For example, if the DMM is not set up to handle temperature sensors, manual scaling will be required. Taking these limitations into account, this is often an acceptablemethod when you need to perform a quick experiment.Strip chart recorderModern versions of the venerable strip chart recorder allow you to capture data from several inputs. They provide a permanent paper record of the data, and because this data is in graphical format, they allow you to easily spot trends. Once set up, most recorders have sufficient internal intelligence to run unattended — without the aid of either an operator or a computer. Drawbacks include a lack of flexibility and relatively low accuracy, which is often constrained to a few percentage points. You can typically perceive only small changes in the pen plots. While recorders perform well when monitoring a few channels over a long period of time, their value can be limited. For example, they are unable to turn another device on or off. Other concerns include pen and paper maintenance, paper supply and data storage, all of which translate into paper overuse and waste. Still, recorders are fairly easy to set up and operate, and offer a permanent record of the data for quick and simple analysis.Scanning digital multimeterSomebenchtop DMMs offer an optional scanning capability. A slot in the rear of the instrument accepts a scanner card that can multiplex between multiple inputs, with 8 to 10 channels of mux being fairly common. DMM accuracy and the functionality inherent in the instrument's front panel are retained. Flexibility is limited in that it is not possible to expand beyond the number of channels available in the expansion slot. An external PC usually handles data acquisition and analysis.PC plug-in cardsPC plug-in cards are single-board measurement systems that take advantage of the ISA or PCI-bus expansion slots in a PC. They often have reading rates as high as 100,000 readings per second. Counts of 8 to 16 channels are common, and acquired data is stored directly into the computer, where it can then be analyzed. Because the card is essentially part of the computer, it is easy to set up tests. PC cards also arerelatively inexpensive, in part, because they rely on the host PC to provide power, the mechanical enclosure and the user interface.Data acquisition optionsIn the downside, PC plug-in cards often have only 12 bits of resolution, so you can't perceive small variations with the input signal. Furthermore, the electrical environment inside a PC tends to be noisy, with high-speed clocks and bus noise radiated throughout. Often, this electrical interference limits the accuracy of the PC plug-in card to that of a handheld DMM .These cards also measure a fairly limited range of dc voltage. To measure other input signals, such as ac voltage, temperature or resistance, you may need some sort of external signal conditioning. Additional concerns include problematic calibration and overall system cost, especially if you need to purchase additional signal conditioning accessories or a PC to accommodate the cards. Taking that into consideration, PC plug-in cards offer an attractive approach to data acquisition if your requirements fall within the capabilities and limitations of the card.Data loggersData loggers are typically stand-alone instruments that, once they are setup, can measure, record and display data without operator or computer intervention. They can handle multiple inputs, in some instances up to 120 channels. Accuracy rivals that found in standalone bench DMMs, with performance in the 22-bit, 0.004-percent accuracy range. Some data loggers have the ability to scale measurements, check results against user-defined limits, and output signals for control.One advantage of using data loggers is their built-in signal conditioning. Most are able to directly measure a number of different inputs without the need for additional signal conditioning accessories. One channel could be monitoring a thermocouple, another a resistive temperature device (RTD) and still another could be looking at voltage.Thermocouple reference compensation for accurate temperature measurement is typically built into the multiplexer cards. A data logger's built-in intelligence helpsyou set up the test routine and specify the parameters of each channel. Once you have completed the setup, data loggers can run as standalone devices, much like a recorder. They store data locally in internal memory, which can accommodate 50,000 readings or more.PC connectivity makes it easy to transfer data to your computer for in-depth analysis. Most data loggers are designed for flexibility and simple configuration and operation, and many provide the option of remote site operation via battery packs or other methods. Depending on the A/D converter technique used, certain data loggers take readings at a relatively slow rate, especially compared to many PC plug-in cards. Still, reading speeds of 250 readings/second are not uncommon. Keep in mind that many of the phenomena being monitored are physical in nature — such as temperature, pressure and flow — and change at a fairly slow rate. Additionally, because of a data logger's superior measurement accuracy, multiple readings and averaging are not necessary, as they often are in PC plug-in solutions.Data acquisition front endsData acquisition front ends are often modular and are typically connected to a PC or controller. They are used in automated test applications for gathering data and for controlling and routing signals in other parts of the test setup. Front end performance can be very high, with speed and accuracy rivaling the best standalone instruments. Data acquisition front ends are implemented in a number of formats, including VXI versions, such as the Agilent E1419A multifunction measurement and control VXI module, and proprietary card cages.. Although front-end cost has been decreasing, these systems can be fairly expensive, and unless you require the high performance they provide, you may find their price to be prohibitive. On the plus side, they do offer considerable flexibility and measurement capability.Data Logger ApplicationsA good, low-cost data logger with moderate channel count (20 - 60 channels) and a relatively slow scan rate is more than sufficient for many of the applications engineers commonly face. Some key applications include:• Product characterization• Thermal profiling of electronic products• Environmental testing; environmental monitoring• Component characteriza tion• Battery testing• Building and computer room monitoring• Process monitoring, evaluation and troubleshooting No single data acquisition system works for all applications. Answering the following questions may help you decide which will best meet your needs:1. Does the system match my application?What is the measurement resolution, accuracy and noise performance? How fast does it scan? What transducers and measurement functions are supported? Is it upgradeable or expandable to meet future needs? How portable is it? Can it operate as a standalone instrument?2. How much does it cost?Is software included, or is it extra? Does it require signal conditioning add-ons? What is the warranty period? How easy and inexpensive is it to calibrate?3. How easy is it to use?Can the specifications be understood? What is the user interface like? How difficult is it to reconfigure for new applications? Can data be transferred easily to new applications? Which application packages are supported?ConclusionData acquisition can range from pencil, paper and a measuring device, to a highly sophisticated system of hardware instrumentation and software analysis tools. The first step for users contemplating the purchase of a data acquisition device or system is to determine the tasks at hand and the desired output, and then select the type and scope of equipment that meets their criteria. All of the sophisticated equipment and analysis tools that are available are designed to help users understand the phenomena they are monitoring. The tools are merely a means to an end.正确选择数据采集系统工程师经常要对很长时间内的很多信号进行监测、画图和分析产生的数据。
数据库设计 英文
Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke
Key Constraints
ssn
since name lot did dname budget
Consider Works_In: An employee can work in many departments; a dept can have many employees. In contrast, each dept has at most one manager, according to the key constraint on Manages.
ssn
lot
Employees supervisor subordinate
Reports_To
Relationship: Association among two or more entities. E.g., Barbara works in the CS department. Relationship Set: Collection of similar relationships.
(in DB) using a set of attributes.
Entity Set: A collection of similar entities. E.g., all employees.
All entities in an entity set have the same set of attributes. (Until we consider ISA hierarchies, anyway!) Each entity set has a key (primary key - SSN). Each attribute has a domain.
数据库设计外文翻译--Java开发2.0：使用 Hibernate Shards 进行切分
本科生毕业设计（论文）外文资料译文（ 2011 届）译文题目Java开发2.0：使用Hibernate Shards 进行切分外文资料译文规范说明一、译文文本要求1．外文译文不少于2000汉字；2．外文译文本文格式参照论文正文规范（标题、字体、字号、图表、原文信息等）；3．外文原文资料信息列文末，对应于论文正文的参考文献部分，标题用“外文原文资料信息”，内容包括：1）外文原文作者；2）书名或论文题目；3）外文原文来源：□出版社或刊物名称、出版时间或刊号、译文部分所在页码□网页地址二、外文原文资料（电子文本或数字化后的图片）：1．外文原文不少于10000印刷字符（图表等除外）；2．外文原文若是纸质的请数字化（图片）后粘贴于译文后的原文资料处，但装订时请用纸质原文复印件附于译文后。
指导教师意见：指导教师签名：年月日一、外文资料译文：Java开发2.0：使用Hibernate Shards 进行切分横向扩展的关系数据库Andrew Glover，作者兼开发人员，Beacon50摘要：Sharding并不适合所有网站，但它是一种能够满足大数据的需求方法。
对于一些商店来说，切分意味着可以保持一个受信任的 RDBMS，同时不牺牲数据可伸缩性和系统性能。
在Java 开发 2.0系列的这一部分中，您可以了解到切分何时起作用，以及何时不起作用，然后开始着手对一个可以处理数 TB 数据的简单应用程序进行切分。
日期：2010年8月31日级别：中级PDF格式：A4和信（64KB的15页）取得Adobe®Reader®软件当关系数据库试图在一个单一表中存储数TB 的数据时，总体性能通常会降低。
索引所有的数据读取，显然是很耗时的，而且其中有可能是写入，也可能是读出。
因为NoSQL 数据商店尤其适合存储大型数据，但是NoSQL 是一种非关系数据库方法。
对于倾向于使用ACID-ity 和实体结构关系数据库的开发人员及需要这种结构的项目来说，切分是一个令人振奋的选方法。
计算机毕业设计外文翻译---数据仓库
DATA WAREHOUSEData warehousing provides architectures and tools for business executives to systematically organize, understand, and use their data to make strategic decisions. A large number of organizations have found that data warehouse systems are valuable tools in today's competitive, fast evolving world. In the last several years, many firms have spent millions of dollars in building enterprise-wide data warehouses. Many people feel that with competition mounting in every industry, data warehousing is the latest must-have marketing weapon —— a way to keep customers by learning more about their needs.“So", you may ask, full of intrigue, “what exactly is a data warehouse?"Data warehouses have been defined in many ways, making it difficult to formulate a rigorous definition. Loosely speaking, a data warehouse refers to a database that is maintained separately from an organization's operational databases. Data warehouse systems allow for the integration of a variety of application systems. They support information processing by providing a solid platform of consolidated, historical data for analysis.According to W. H. Inmon, a leading architect in the construction of data warehouse systems, “a data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management's decision making process." This short, but comprehensive definition presents the major features of a data warehouse. The four keywords, subject-oriented, integrated, time-variant, and nonvolatile, distinguish data warehouses from other data repository systems, such as relational database systems, transaction processing systems, and file systems. Let's take a closer look at each of these key features.(1)Subject-oriented: A data warehouse is organized around major subjects, such as customer, vendor, product, and sales. Rather than concentrating on the day-to-day operations and transaction processing of an organization, a data warehouse focuses on the modeling and analysis of data for decision makers. Hence, data warehouses typically provide a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process.(2)Integrated: A data warehouse is usually constructed by integrating multiple heterogeneous sources, such as relational databases, flat files, and on-line transaction records. Data cleaning and data integration techniques are applied to ensure consistency in naming conventions, encoding structures, attribute measures, and so on..(3)Time-variant: Data are stored to provide information from a historical perspective (e.g., the past 5-10 years). Every key structure in the data warehouse contains, either implicitly or explicitly, an element of time.(4)Nonvolatile: A data warehouse is always a physically separate store of data transformed from the application data found in the operational environment. Due to this separation, a data warehouse does not require transaction processing, recovery, and concurrency control mechanisms. It usually requires only two operations in data accessing: initial loading of data and access of data..In sum, a data warehouse is a semantically consistent data store that serves as a physical implementation of a decision support data model and stores the information on which an enterprise needs to make strategic decisions. A data warehouse is also often viewed as an architecture, constructed by integrating data from multiple heterogeneous sources to support structured and/or ad hoc queries, analytical reporting, and decision making.“OK", you now ask, “what, then, is data warehousing?"Based on the above, we view data warehousing as the process of constructing and using data warehouses. The construction of a data warehouse requires data integration, data cleaning, and data consolidation. The utilization of a data warehouse often necessitates a collection of decision support technologies. This allows “knowledge workers" (e.g., managers, analysts, and executives) to use the warehouse to quickly and conveniently obtain an overview of the data, and to make sound decisionsbased on information in the warehouse. Some authors use the term “data warehousing" to refer only to the process of data warehouse construction, while the term warehouse DBMS is used to refer to the management and utilization of data warehouses. We will not make this distinction here.“How are organizations using the information from data warehouses?" Many organizations are using this information to support business decision making activities, including:(1) increasing customer focus, which includes the analysis of customer buying patterns (such as buying preference, buying time, budget cycles, and appetites for spending).(2) repositioning products and managing product portfolios by comparing the performance of sales by quarter, by year, and by geographic regions, in order to fine-tune production strategies.(3) analyzing operations and looking for sources of profit.(4) managing the customer relationships, making environmental corrections, and managing the cost of corporate assets.Data warehousing is also very useful from the point of view of heterogeneous database integration. Many organizations typically collect diverse kinds of data and maintain large databases from multiple, heterogeneous, autonomous, and distributed information sources. To integrate such data, and provide easy and efficient access to it is highly desirable, yet challenging. Much effort has been spent in the database industry and research community towards achieving this goal.The traditional database approach to heterogeneous database integration is to build wrappers and integrators (or mediators) on top of multiple, heterogeneous databases. A variety of data joiner and data blade products belong to this category. When a query is posed to a client site, a metadata dictionary is used to translate the query into queries appropriate for the individual heterogeneous sites involved. These queries are then mapped and sent to local query processors. The results returned from the different sites are integrated into a global answer set. This query-driven approach requires complex information filtering and integration processes, and competes for resources with processing at local sources. It is inefficient and potentially expensive for frequent queries, especially for queries requiring aggregations.Data warehousing provides an interesting alternative to the traditional approach of heterogeneous database integration described above. Rather than using a query-driven approach, data warehousing employs an update-driven approach in which information from multiple, heterogeneous sources is integrated in advance and stored in a warehouse for direct querying and analysis. Unlike on-line transaction processing databases, data warehouses do not contain the most current information. However, a data warehouse brings high performance to the integrated heterogeneous database system since data are copied, preprocessed, integrated, annotated, summarized, and restructured into one semantic data store. Furthermore, query processing in data warehouses does not interfere with the processing at local sources. Moreover, data warehouses can store and integrate historical information and support complex multidimensional queries. As a result, data warehousing has become very popular in industry.1.Differences between operational database systems and data warehousesSince most people are familiar with commercial relational database systems, it is easy to understand what a data warehouse is by comparing these two kinds of systems.The major task of on-line operational database systems is to perform on-line transaction and query processing. These systems are called on-line transaction processing (OLTP) systems. They cover most of the day-to-day operations of an organization, such as, purchasing, inventory, manufacturing, banking, payroll, registration, and accounting. Data warehouse systems, on the other hand, serve users or “knowledge workers" in the role of data analysis and decision making. Such systems can organize and present data in various formats in order to accommodate the diverse needs of the different users. These systems are known as on-line analytical processing (OLAP) systems.The major distinguishing features between OLTP and OLAP are summarized as follows.(1)Users and system orientation: An OLTP system is customer-oriented and is used for transaction and query processing by clerks, clients, and information technology professionals. An OLAP system is market-oriented and is used for data analysis by knowledge workers, including managers, executives, and analysts.(2)Data contents: An OLTP system manages current data that, typically, are too detailed to be easily used for decision making. An OLAP system manages large amounts of historical data, provides facilities for summarization and aggregation, and stores and manages information at different levels of granularity. These features make the data easier for use in informed decision making.(3)Database design: An OLTP system usually adopts an entity-relationship (ER) data model and an application -oriented database design. An OLAP system typically adopts either a star or snowflake model, and a subject-oriented database design.(4)View: An OLTP system focuses mainly on the current data within an enterprise or department, without referring to historical data or data in different organizations. In contrast, an OLAP system often spans multiple versions of a database schema, due to the evolutionary process of an organization. OLAP systems also deal with information that originates from different organizations, integrating information from many data stores. Because of their huge volume, OLAP data are stored on multiple storage media.(5). Access patterns: The access patterns of an OLTP system consist mainly of short, atomic transactions. Such a system requires concurrency control and recovery mechanisms. However, accesses to OLAP systems are mostly read-only operations (since most data warehouses store historical rather than up-to-date information), although many could be complex queries.Other features which distinguish between OLTP and OLAP systems include database size, frequency of operations, and performance metrics and so on.2.But, why have a separate data warehouse?“Since operational databases store huge amounts of data", you observe, “why not perform on-line analytical processing directly on such databases instead of spending additional time and resources to construct a separate data warehouse?"A major reason for such a separation is to help promote the high performance of both systems. An operational database is designed and tuned from known tasks and workloads, such as indexing and hashing using primary keys, searching for particular records, and optimizing “canned" queries. On the other hand, data warehouse queries are often complex. They involve the computation of large groups of data at summarized levels, and may require the use of special data organization, access, and implementation methods based on multidimensional views. Processing OLAP queries in operational databases would substantially degrade the performance of operational tasks.Moreover, an operational database supports the concurrent processing of several transactions. Concurrency control and recovery mechanisms, such as locking and logging, are required to ensure the consistency and robustness of transactions. An OLAP query often needs read-only access of data records for summarization and aggregation. Concurrency control and recovery mechanisms, if applied for such OLAP operations, may jeopardize the execution of concurrent transactions and thus substantially reduce the throughput of an OLTP system.Finally, the separation of operational databases from data warehouses is based on the different structures, contents, and uses of the data in these two systems. Decision support requires historical data, whereas operational databases do not typically maintain historical data. In this context, the data in operational databases, though abundant, is usually far from complete for decision making. Decision support requires consolidation (such as aggregation and summarization) of data from heterogeneous sources, resulting in high quality, cleansed and integrated data. In contrast, operational databases contain only detailed raw data, such as transactions, which need to be consolidated before analysis. Since the two systems provide quite different functionalities and require different kinds of data, it is necessary to maintain separate databases.数据仓库数据仓库为商务运作提供了组织结构和工具，以便系统地组织、理解和使用数据进行决策。
2160123 数据库设计(中英文)(2011)
二．教学基本要求 在本课程设计的实践过程中，要求学生能够： (1) 在 SQL Server 上设计并实现一个数据库应用系统。用户界面可以使用
Web 界面或窗口界面。可使用任意一种技术进行数据库连接与操作（例如，JDBC 或 ODBC）。
(2) 根据课程设计 2 周时间的安排选择适当大小的设计课题，课题的选择可 以是实际问题，也可以是虚构的问题。
Database design: E/R model design
4.
Database design: database normalization
5.
Database design: creation of tables and constraints
6.
Database design: creation of indexes, triggers, stored procedures
Prerequisite:
Database Principles, GIS Design
1. Objective Database knowledge and technology is one of the major fields in Computer
Science, and databases today are the most effective means of data management in every business. This course is one of the compulsory courses of the direction of Computer and Information Technology of the major of Computer Science and Technology, and is a comprehensive and integrated practice after completing the course of Database Principles. The course aims to deepen the basic understanding of the underlying database theory and knowledge, master the basic methods for software design using the database, and improve the use of the database to solve practical problems.
外文翻译---数据仓库技术
附录1 外文原文Data warehouse techniqueThe data warehouse says allThe data warehouse is an environment, not a product. It provides the decision that customer used for current history data supports, these data is very difficult in traditional operation type database or can't get, say more tangibly, the data warehouse is a kind of system construction. Data warehouse than it customer relation the management is a concept that is been familiar with by person, it is 1991 the United States an information engineering learns what house W.H. Inmon Doctor put forward, its definition is" the data warehouse is a support decision the process faces to the topic of, gather of, at any time but change of, the last long data gathers".The technique system construction of the data warehouse The data of obtains mold piece: Used for obtaining the data from the source document with the source database, combine the proceeding sweep, delivering, adding it to data warehouse database inside.· Data management mold piece: Used for the movement of the management data warehouse.· Delivers mold piece: Used for the other warehouse in direction with assign the data warehouse data in the exterior system.· The data is in the center a mold piece: The end customer in direction tool that used for the method provides the interview data warehouse database.· Data interview mold piece: Used for providing the interview for the end customer of the business enterprise with the tool of the analysis data warehouse data.· Design mold piece: Used for the design data warehouse database.· Information catalogue mold piece: Used for governor to provide with the customer relevant saving contents in data warehouse data in the database with meaning information.How to establish the data warehouseCurrent, the internal calculator in business enterprise system is mutually independent, the data rule( legitimacy) demand of the system that have is affirmed from the other system, various data lacks to gather sex, conduct and actions trend, the data warehouse technique is an one of the most emollient way to makes these data gathered get ups, the data warehouse establishes can at logical realize various system interaction operation, this lay the foundation for the modern college in developments, also leads for the college layer science decision offering guarantees powerfully. Theprocess that establish in the data warehouse needs below step:1. Establish the data model to the end business need. The design of the data model not only consider only to the first topic, but also looks after both sides the need of the other management in college decision topic to searches the need of the topic with every kind of data, statement.2. The certain topic proceeding data sets up the mold. According to the decision need certain topic, choice data source, proceed logic construction design.3. The database of the design data warehouse. Put great emphasis on the saving construction in physics that apply in the topic development data warehouse inside data.4. Definition data source. According to the topic data model, choose different operation type database as the data source.5. Establish the model for a data. The model made sure into the data scope of the data warehouse, and with provision of relevant data. Complete a data, can let customer known, the data warehouse inside has actually what data, the data gathers the level of structure with how detailed degree is, can provide what information, how these information are carried calculates with organizes etc..6. Take( Extract), convert( Transmit), add from the operation type database inside take out the data that carry( Load) the database inside arrive the data warehouse.7. Choice data interview analysis tool, the customer will use the saving information within these toolses interview data warehouse, realizing decision support need.The data scoops out the techniqueAlong with the database technical develop continuously and extensive application in each profession in system in management in database, the backlog enlarges in the nasty play in amount of data in the database, but among them can use directly however opposite less in amount of information.People have been hoping can to conceal in the superficial information in these data, proceed many level of structures analyze, for the purpose of better land utilization acquire the benefit to operate in the business with these data, increase the information of the social competition ability.Current every kind of database management system although can realizes efficiently the data record into and search, statistics to wait the function, can't discover relation existed in the data with regulation, resulted in like this and then a kind of data Bang and knowledge needy keep both of phenomenon. According to the inquisition, the data collections increase with saving with every year 130% speed, but in the data only have 2% data to is analyzed availably. This exploitation that scoop out provided the vast space for the data .To the 2004, apply to attain USD1,000,000,000 in the data of the electronic commerce market of scooping out the tool.附录2 外文资料译文数据仓库技术数据仓库概述数据仓库是一个环境，而不是一件产品。
数据库设计中英文术语表
数据库设计中英文术语表正文1.Access method（访问方法）：此步骤包括从文件中存储和检索记录。
2.Alias（别名）：某属性的另一个名字。
在SQL中，可以用别名替换表名。
3.Alternate keys（备用键，ER/关系模型）：在实体/表中没有被选为主健的候选键。
4.Anomalies（异常）参见更新异常（update anomalies）5.Application design（应用程序设计）：数据库应用程序生命周期的一个阶段，包括设计用户界面以及使用和处理数据库的应用程序。
6.Attribute（属性）（关系模型）：属性是关系中命名的列。
7.Attribute（属性）（ER模型）：实体或关系中的一个性质。
8.Attribute inheritance（属性继承）：子类成员可以拥有其特有的属性，并且继承那些与超类有关的属性的过程。
9.Base table（基本表）：一个命名的表，其记录物理的存储在数据库中。
10.Binary relationship（二元关系）：一个ER术语，用于描述两个实体间的关系。
例如，panch Has Staff。
11.Bottom-up approach（自底向上方法）：用于数据库设计，一种设计方法学，他从标识每个设计组建开始，然后将这些组件聚合成一个大的单元。
在数据库设计中，可以从表示属性开始底层设计，然后将这些属性组合在一起构成代表实体和关系的表。
12.Business rules（业务规则）：由用户或数据库的管理者指定的附加规则。
13.Candidate key（候选键，ER关系模型）：仅包含唯一标识实体所必须得最小数量的属性/列的超键。
14.Cardinality（基数）：描述每个参与实体的可能的关系数目。
15.Centralized approach（集中化方法,用于数据库设计）：将每个用户试图的需求合并成新数据库应用程序的一个需求集合16.Chasm trap（深坑陷阱）：假设实体间存在一根，但某些实体间不存在通路。
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
外文翻译：索引原文来源：Thomas Kyte．Expert Oracle Database Architecture ．2nd Edition．译文正文：什么情况下使用B*树索引?我并不盲目地相信“法则”（任何法则都有例外），对于什么时候该用B*索引，我没有经验可以告诉你。
为了证明为什么这个方面我无法提供任何经验，下面给出两种等效作法：•使用B*树索引，如果你想通过索引的方式去获得表中所占比例很小的那些行。
•使用B *树索引，如果你要处理的表和索引许多可以代替表中使用的行。
这些规则似乎提供相互矛盾的意见，但在现实中，他们不是这样的，他们只是涉及两个极为不同的情况。
有两种方式使用上述意见给予索引：•作为获取表中某些行的手段。
你将读取索引去获得表中的某一行。
在这里你想获得表中所占比例很小的行。
•作为获取查询结果的手段。
这个索引包含足够信息来回复整个查询，我们将不用去查询全表。
这个索引将作为该表的一个瘦版本。
还有其他方式—例如，我们使用索引去检索表的所有行，包括那些没有建索引的列。
这似乎违背了刚提出的两个规则。
这种方式获得将是一个真正的交互式应用程序。
该应用中，其中你将获取其中的某些行，并展示它们，等等。
你想获取的是针对初始响应时间的查询优化，而不是针对整个查询吞吐量的。
在第一种情况（也就是你想通过索引获得表中一小部分的行）预示着如果你有一个表T （使用与早些时候使用过的相一致的表T）,然后你获得一个像这样查询的执行计划：ops$tkyte%ORA11GR2> set autotrace traceonly explainops$tkyte%ORA11GR2> select owner, status2 from t3 where owner = USER;Execution Plan----------------------------------------------------------Plan hash value: 1049179052------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes |------------------------------------------------------------------| 0 | SELECT STATEMENT | | 2120 | 23320 || 1 | TABLE ACCESS BY INDEX ROWID |T | 2120 | 23320 || *2 | INDEX RANGE SCAN | DESC_T_IDX | 8 | |------------------------------------------------------------------Predicate Information (identified by operation id):---------------------------------------------------2 - access(SYS_OP_DESCEND("OWNER")=SYS_OP_DESCEND(USER@!))filter(SYS_OP_UNDESCEND(SYS_OP_DESCEND("OWNER"))=USER@!)你应该访问到该表的一小部分。
这个问题在这里看是INDEX (RANGE SCAN) 紧跟在TABLE ACCESS BY INDEX ROWID之后。
这也意味着Oracle先读取索引，然后获取索引项。
该索引项将执行一个数据库块读（逻辑或者物理的I/O）去获取行数据。
如果你想通过索引去访问数据表T中的大部分数据，这不是最高效的方式（我们将很快定义什么是大部分的数据）。
第二种情况，（也就是你想通过索引去代替表），你将通过索引去处理100%（事实上可以是任何比例）的行。
也许你想通过索引索引去获得一个缩小版的表。
接下来的查询证明了这种方式：ops$tkyte%ORA11GR2> select count(*)2 from t3 where owner = user;Execution Plan----------------------------------------------------------Plan hash value: 293504097---------------------------------------------------------------------------|Id | Operation. | Name | Rows | Bytes .| Cost (%CPU)| Time |---------------------------------------------------------------------------| 0 | SELECT STA TEMENT | | 1 | 6 | 17 (0) | 00:00:01 || 1 | SORT AGGREGAT E | | 1 | 6 | .. | .|| * 2 | INDEX RANGE SCAN | T_IDX | 2 120 | 12720 | 17 (0) | 00: 00: 01 |---------------------------------------------------------------------------Predicate Information (identified by operation id):---------------------------------------------------2 - access("OWNER"=USER@!)这里，仅仅是使用索引去作为查询的返回集-现在再也不在乎我们只通过索引的方式，想访问多少比例的行。
从执行计划中可以看到，查询语句从未访问过表，仅仅扫描索引结构本身。
理解两种概念的区别很重要。
当执行TABLE ACCESS BY ROWID操作时，我们必须确保仅访问表中一小部分的块，也就相当于仅访问一小部分的行或者是尽量块地获取第一的数据。
（最终的用户将会为了这几行数据等得不耐烦的）。
如果想通过访问比较高比例的行（所占比例高于20%），使用B*索引的话，它将花费比全表扫描更多的时间。
使用第二种查询方式，那些在索引中可以找到所需结果的，情况就完全不同了。
我们读取索引块，然后拾取其中的很多行进行处理，如此继续下一个索引块，从不访问表。
某些情况下，还可以在索引上执行一个快速全面扫描。
快速全面扫描是指，数据库不按特定的顺序读取索引块，只是开始读取它们。
这里不再是将索引只当一个索引，此时更像是一个表。
如果采用全表扫描，将不会按索引项来顺序获取行。
一般来讲，B*树索引将会被放在查询时频繁使用的列上。
而且我们希望从表中只返回少量的数据或者最终用户的请求想立即得到反馈。
在一个瘦表（也就是一个含有很少的列或者列很小）中，这个比例可能很小。
一个查询,使用该索引应该可以在表中取回约2% ~ 3%或更少的行。
在一个胖表（也就是含有很多列或者列很宽）中，这个比例将一直上升到该表的20%~25%。
以上建议并不对每个人都有作用。
这个比例并不直观，但很精确。
索引根据索引键进行排序存储。
索引会按键的有序顺序进行访问。
索引指向的块都随机存储在堆中。
因此，我们通过索引访问表时，会执行大类分散、随机的I/O。
这里的“分散”是指，索引会告诉我们读取块1，然后是块1000,块205，块1，块1032，块1等等。
它们不会要求我们按照块1，块2然后块3的方式。
我们将以一种非常随意的方式读取和重新读取块，这种块I/O可能非常慢。
让我们看一下简化版的例子，假设我们通过索引读取一个瘦表，而且要读取表中的20%的行。
若这个表中有100000行，这个表得20%就是20000行。
如果行大小约为80个字节，在一个块大小为8KB的数据库中，我们将在每个块中获得100行数据。
这也就意味着这个表有1000个块。
了解这些，计算起来就很容易了。
我们想通过索引去读取2000行，这也就意味着几乎相当于20000次的TABLE ACCESS BY ROWID 操作。
这将导致执行这个操作要处理20000个表块。
不过，这个表总共才只有1000块。
我们将对表的每个块要执行读和处理20次。
即时把行的大小提高到一个数量级，达到每行800字节，这样每块有10行，那样这个表现在有10000块。
要通过索引20000行，仍要求我们把每一块平均读取2次。
在这种情况下，全表扫描就比使用索引高效得多。
因为每个块只会命中一次。
如果把查询使用这个索引来访问数据，效率都不会高，除非对应800字节的行，平均只访问表中不到5%的数据（这样一来，我们访问的大概为5000块），如果是80字节的行，则访问的数据应当只占更小的百分比（大约0.5%或更少）。
什么情况下使用位图索引?位图索引是最适合于低相异基数数据的情形（也就是说，与整个数据集得基数相比，这个数据只有很少几个不同的值）。
对此作出量化是不太可能的——换句话说，就是很难定义这个低相异基数数据有到底多么不同。
在一个有几千条记录的数据集中，2就是一个低相异基数，但是在一个只有两行记录的数据表中，2就不再是低相异基数了。
而在一个上千万或者上亿条记录的表中，甚至100,000都能作为一个低相异基数。
所以，多大才算是低相异基数，这要相对于结果集得大小来说。
这里是指行集中不同项的个数除以行数应该是一个很小的数（接近于0）。
例如，GENDER列可能取值为M、F和NULL。
如果一个表中有20,000条员工记录，那么你将会发现3/20,000=0.00015。
同样地，10,000,000中100,000个不同值得比例为0.01,——同样，值很小。
这些列就可以建立位图索引。
他们可能不合适建立B*树索引，因为每个值可能会获取表中的大量数据。
如同前面所述，B*数索引一般来讲是选择性的。
位图索引不带有选择性的——相反，一般是“没有选择性”的。
位图索引在有很多即时查询的时候极其有用，尤其是在查询涉及很多列或者会生成诸如COUNT之类的聚会。
例如，假设有一个含有GENDER，LOCATION，和AGE_GROUP三个字段的大表。
在这个表中，GENDER的值为M或者F，LOCATION可以选取1到50之间的值，AGE_GROUP为代表18岁及以下，19-25，26-30，31-40，和40岁及以上的代码。