软件工程专业软件数据库的面向对象的视角大学毕业论文外文文献翻译及原文

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文献、资料中文题目：专业软件数据库的面向对象的视角

文献、资料英文题目：Software Database An Object-Oriented Perspective

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翻译日期： 2017.02.14

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A HISTORICAL PERSPECTIVE

From the earliest days of computers, storing and manipulating data have been a major application focus. The first general-purpose DBMS was designed by Charles Bachman at General Electric in the early 1960s and was called the Integrated Data Store. It formed the basis for the network data model, which was standardized by the Conference on Data Systems Languages (CODASYL) and strongly influenced database systems through the 1960s. Bachman was the fi rst recipient of ACM’s Turing Award (the computer science equivalent of a Nobel prize) for work in the database area; he received the award in 1973.

本科毕业设计外文文献翻译 英文题目：Software Database An Object-Oriented Perspective. 中文题目：软件数据库的面向对象的视角

In the late 1960s, IBM developed the Information Management System (IMS) DBMS, used even today in many major installations. IMS formed the basis for an alternative data representation framework called the hierarchical data model. The SABRE system for making airline reservations was jointly developed by American Airlines and IBM around the same time, and it allowed several people to access the same data through computer network. Interestingly, today the same SABRE system is used to power popular Web-based travel services such as Travelocity!

In 1970, Edgar Codd, at IBM’s San Jose Research Laboratory, proposed a new data representation framework called the relational data model. This proved to be a watershed in the development of database systems: it sparked rapid development of several DBMSs based on the relational model, along with a rich body of theoretical results that placed the field on a firm foundation. Codd won the 1981 Turing Award for his seminal work. Database systems matured as an academic discipline, and the popularity of relational DBMSs changed the commercial landscape. Their benefits were widely recognized, and the use of DBMSs for managing corporate data became standard practice.

In the 1980s, the relational model consolidated its position as the dominant DBMS paradigm, and database systems continued to gain widespread use. The SQL query language for relational databas es, developed as part of IBM’s System R project, is now the standard query language. SQL was standardized in the late 1980s, and the current standard, SQL-92, was adopted by the American National Standards Institute (ANSI) and International Standards Organization (ISO). Arguably, the most widely used form of concurrent programming is the concurrent execution of database programs (called transactions). Users write programs as if they are to be run by themselves, and the responsibility for running them concurrently is given to the DBMS. James Gray won the 1999 Turing award for his contributions to the field of transaction management in a DBMS.

In the late 1980s and the 1990s, advances have been made in many areas of database systems. Considerable research has been carried out into more powerful query languages and richer data models, and there has been a big emphasis on supporting complex analysis of data from all parts of an enterprise. Several vendors (e.g., IBM’s DB2, Oracle 8, Informix UDS) have extended their systems with the ability to store new data types such as

images and text, and with the ability to ask more complex queries. Specialized systems have been developed by numerous vendors for creating data warehouses, consolidating data from several databases, and for carrying out specialized analysis.

An interesting phenomenon is the emergence of several enterprise resource planning(ERP) and management resource planning (MRP) packages, which add a substantial layer of application-oriented features on top of a DBMS. Widely used packages include systems from Baan, Oracle, PeopleSoft, SAP, and Siebel. These packages identify a set of common tasks (e.g., inventory management, human resources planning, financial analysis) encountered by a large number of organizations and provide a general application layer to carry out these tasks. The data is stored in a relational DBMS, and the application layer can be customized to different companies, leading to lower Introduction to Database Systems overall costs for the companies, compared to the cost of building the application layer from scratch. Most significantly, perhaps, DBMSs have entered the Internet Age. While the first generation of Web sites stored their data exclusively in operating systems files, the use of a DBMS to store data that is accessed through a Web browser is becoming widespread. Queries are generated through Web-accessible forms and answers are formatted using a markup language such as HTML, in order to be easily displayed in a browser. All the database vendors are adding features to their DBMS aimed at making it more suitable for deployment over the Internet. Database management continues to gain importance as more and more data is brought on-line, and made ever more accessible through computer networking. Today the field is being driven by exciting visions such as multimedia databases, interactive video, digital libraries, a host of scientific projects such as the human genome mapping effort and NASA’s Earth Observation System project, and the desire of companies to consolidate their decision-making processes and mine their data repositories for useful information about their businesses. Commercially, database manage- ment systems represent one of the largest and most vigorous market segments. Thusthes- tudy of database systems could prove to be richly rewarding in more ways than one!

INTRODUCTION TO PHYSICAL DATABASE DESIGN

Like all other aspects of database design, physical design must be guided by the nature of the data and its intended use. In particular, it is important to understand the typical workload that the database must support; the workload consists of a mix of queries and updates. Users also have certain requirements about how fast certain queries or updates must run or how many transactions must be processed per second. The workload description and users’ performance requirements are the basis on which a number of decisions have to be made during physical database design.

To create a good physical database design and to tune the system for performance in response to evolving user requirements, the designer needs to understand the workings of a DBMS, especially the indexing and query processing techniques supported by the DBMS. If the database is expected to be accessed concurrently by many users, or is a distributed database, the task becomes more complicated, and other features of a DBMS come into play.

DATABASE WORKLOADS

The key to good physical design is arriving at an accurate description of the expected workload. A workload description includes the following elements:

1. A list of queries and their frequencies, as a fraction of all queries and updates.

2. A list of updates and their frequencies.

3. Performance goals for each type of query and update.

For each query in the workload, we must identify:

Which relations are accessed.

Which attributes are retained (in the SELECT clause).

Which attributes have selection or join conditions expressed on them (in the WHERE clause) and how selective these conditions are likely to be. Similarly, for each update in the workload, we must identify:

Which attributes have selection or join conditions expressed on them (in the WHERE

clause) and how selective these conditions are likely to be.

The type of update (INSERT, DELETE, or UPDATE) and the updated relation.

For UPDATE commands, the fields that are modified by the update.

Remember that queries and updates typically have parameters, for example, a debit or credit operation involves a particular account number. The values of these parameters determine selectivity of selection and join conditions.

Updates have a query component that is used to find the target tuples. This component can benefit from a good physical design and the presence of indexes. On the other hand, updates typically require additional work to maintain indexes on the attributes that they modify. Thus, while queries can only benefit from the presence of an index, an index may either speed up or slow down a given update. Designers should keep this trade-offer in mind when creating indexes.

NEED FOR DATABASE TUNING

Accurate, detailed workload information may be hard to come by while doing the initial design of the system. Consequently, tuning a database after it has been designed and deployed is important—we must refine the initial design in the light of actual usage patterns to obtain the best possible performance.

The distinction between database design and database tuning is somewhat arbitrary.

We could consider the design process to be over once an initial conceptual schema is designed and a set of indexing and clustering decisions is made. Any subsequent changes to the conceptual schema or the indexes, say, would then be regarded as a tuning activity. Alternatively, we could consider some refinement of the conceptual schema (and physical design decisions affected by this refinement) to be part of the physical design process.

Where we draw the line between design and tuning is not very important.

OVERVIEW OF DATABASE TUNING After the initial phase of database design, actual use of the database provides a

valuable source of detailed information that can be used to refine the initial design. Many of the original assumptions about the expected workload can be replaced by observed usage patterns; in general, some of the initial workload specification will be validated, and some of it will turn out to be wrong. Initial guesses about the size of data can be replaced with actual statistics from the system catalogs (although this information will keep changing as the system evolves). Careful monitoring of queries can reveal unexpected problems; for example, the optimizer may not be using some indexes as intended to produce good plans.

Continued database tuning is important to get the best possible performance.

TUNING THE CONCEPTUAL SCHEMA

In the course of database design, we may realize that our current choice of relation schemas does not enable us meet our performance objectives for the given workload with any (feasible) set of physical design choices. If so, we may have to redesign our conceptual schema (and re-examine physical design decisions that are affected by the changes that we make).

We may realize that a redesign is necessary during the initial design process or later, after the system has been in use for a while. Once a database has been designed and populated with data, changing the conceptual schema requires a significant effort in terms of mapping the contents of relations that are affected. Nonetheless, it may sometimes be necessary to revise the conceptual schema in light of experience with the system. We now consider the issues involved in conceptual schema (re)design from the point of view of performance.

Several options must be considered while tuning the conceptual schema:

We may decide to settle for a 3NF design instead of a BCNF design.

If there are two ways to decompose a given schema into 3NF or BCNF, our choice should be guided by the workload.

Sometimes we might decide to further decompose a relation that is already in BCNF.

In other situations we might denormalize. That is, we might choose to replace a

collection of relations obtained by a decomposition from a larger relation with the original (larger) relation, even though it suffers from some redundancy problems. Alternatively, we might choose to add some fields to certain relations to speed up some important queries, even if this leads to a redundant storage of some information (and consequently, a schema that is in neither 3NF nor BCNF).

This discussion of normalization has concentrated on the technique of decomposition, which amounts to vertical partitioning of a relation. Another technique to consider is horizontal partitioning of a relation, which would lead to our having two relations with identical schemas. Note that we are not talking about physically partitioning the cuples of a single relation; rather, we want to create two distinct relations (possibly with different constraints and indexes on each).

Incidentally, when we redesign the conceptual schema, especially if we are tuning an existing database schema, it is worth considering whether we should create views to mask these changes from users for whom the original schema is more natural.

TUNING QUERIES AND VIEWS

If we notice that a query is running much slower than we expected, we have to examine the query carefully to end the problem. Some rewriting of the query, perhaps in conjunction with some index tuning, can often ?x the problem. Similar tuning may be called for if queries on some view run slower than expected.

When tuning a query, the first thing to verify is that the system is using the plan that you expect it to use. It may be that the system is not finding the best plan for a variety of reasons. Some common situations that are not handled efficiently by many optimizers follow:

A selection condition involving null values.

Selection conditions involving arithmetic or string expressions or conditions using the or connective. For example, if we have a condition E.age = 2*D.age in the WHERE clause, the optimizer may correctly utilize an available index on E.age but fail to utilize an available index on D.age. Replacing the condition by E.age/2=D.age would reverse the

situation.

Inability to recognize a sophisticated plan such as an index-only scan for an aggregation query involving a GROUP BY clause.

If the optimizer is not smart enough to and the best plan (using access methods and evaluation strategies supported by the DBMS), some systems allow users to guide the choice of a plan by providing hints to the optimizer; for example, users might be able to force the use of a particular index or choose the join order and join method. A user who wishes to guide optimization in this manner should have a thorough understanding of both

optimization and the capabilities of the given DBMS.

(8)OTHER TOPICS

MOBILE DATABASES

The availability of portable computers and wireless communications has created a new breed of nomadic database users. At one level these users are simply accessing a database through a network, which is similar to distributed DBMSs. At another level the network as well as data and user characteristics now have several novel properties, which affect basic assumptions in many components of a DBMS, including the query engine, transaction manager, and recovery manager.

Users are connected through a wireless link whose bandwidth is ten times less than Ethernet and 100 times less than ATM networks. Communication costs are therefore significantly higher in proportion to I/O and CPU costs.

Users’ locations are constantly changing, and mobile computers ha ve a limited battery life. Therefore, the true communication costs is connection time and battery usage in addition to bytes transferred, and change constantly depending on location. Data is frequently replicated to minimize the cost of accessing it from different locations.

As a user moves around, data could be accessed from multiple database servers within a single transaction. The likelihood of losing connections is also much greater than in a traditional network. Centralized transaction management may therefore be impractical, especially if some data is resident at the mobile computers. We may in fact have to give up

on ACID transactions and develop alternative notions of consistency for user programs. MAIN MEMORY DA TABASES

The price of main memory is now low enough that we can buy enough main memory to hold the entire database for many applications; with 64-bit addressing, modern CPUs

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Inventory management Inventory Control On the so-called "inventory control", many people will interpret it as a "storage management", which is actually a big distortion. The traditional narrow view, mainly for warehouse inventory control of materials for inventory, data processing, storage, distribution, etc., through the implementation of anti-corrosion, temperature and humidity control means, to make the custody of the physical inventory to maintain optimum purposes. This is just a form of inventory control, or can be defined as the physical inventory control. How, then, from a broad perspective to understand inventory control? Inventory control should be related to the company's financial and operational objectives, in particular operating cash flow by optimizing the entire demand and supply chain management processes (DSCM), a reasonable set of ERP control strategy, and supported by appropriate information processing tools, tools to achieved in ensuring the timely delivery of the premise, as far as possible to reduce inventory levels, reducing inventory and obsolescence, the risk of devaluation. In this sense, the physical inventory control to achieve financial goals is just a means to control the entire inventory or just a necessary part; from the perspective of organizational functions, physical inventory control, warehouse management is mainly the responsibility of The broad inventory control is the demand and supply chain management, and the whole company's responsibility. Why until now many people's understanding of inventory control, limited physical inventory control? The following two reasons can not be ignored: First, our enterprises do not attach importance to inventory control. Especially those who benefit relatively good business, as long as there is money on the few people to consider the problem of inventory turnover. Inventory control is simply interpreted as warehouse management, unless the time to spend money, it may have been to see the inventory problem, and see the results are often very simple procurement to buy more, or did not do warehouse departments . Second, ERP misleading. Invoicing software is simple audacity to call it ERP, companies on their so-called ERP can reduce the number of inventory, inventory control, seems to rely on their small software can get. Even as SAP, BAAN ERP world, the field of

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Statistical hypothesis testing Adriana Albu，Loredana Ungureanu Politehnica University Timisoara,adrianaa@aut.utt.ro Politehnica University Timisoara,loredanau@aut.utt.ro Abstract In this article,we present a Bayesian statistical hypothesis testing inspection, testing theory and the process Mentioned hypothesis testing in the real world and the importance of, and successful test of the Notes. Key words Bayesian hypothesis testing; Bayesian inference;Test of significance Introduction A statistical hypothesis test is a method of making decisions using data, whether from a controlled experiment or an observational study (not controlled). In statistics, a result is called statistically significant if it is unlikely to have occurred by chance alone, according to a pre-determined threshold probability, the significance level. The phrase "test of significance" was coined by Ronald Fisher: "Critical tests of this kind may be called tests of significance, and when such tests are available we may discover whether a second sample is or is not significantly different from the first."[1] Hypothesis testing is sometimes called confirmatory data analysis, in contrast to exploratory data analysis. In frequency probability,these decisions are almost always made using null-hypothesis tests. These are tests that answer the question Assuming that the null hypothesis is true, what is the probability of observing a value for the test statistic that is at [] least as extreme as the value that was actually observed?) 2 More formally, they represent answers to the question, posed before undertaking an experiment,of what outcomes of the experiment would lead to rejection of the null hypothesis for a pre-specified probability of an incorrect rejection. One use of hypothesis testing is deciding whether experimental results contain enough information to cast doubt on conventional wisdom. Statistical hypothesis testing is a key technique of frequentist statistical inference. The Bayesian approach to hypothesis testing is to base rejection of the hypothesis on the posterior probability.[3][4]Other approaches to reaching a decision based on data are available via decision theory and optimal decisions. The critical region of a hypothesis test is the set of all outcomes which cause the null hypothesis to be rejected in favor of the alternative hypothesis. The critical region is usually denoted by the letter C. One-sample tests are appropriate when a sample is being compared to the population from a hypothesis. The population characteristics are known from theory or are calculated from the population.

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