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DATABASE. SYSTEM CONCEPTS. SIXTH EDITION. Abraham Silberschatz. Yale University. Henry F. Korth. Lehigh University. S. Sudarshan. Apago PDF Enhancer zetom.info Page i 12/3/09 PM In this, the sixth edition of Database System Concepts, we have retained the. Codes for Labs and Study Materials. Contribute to MITCSE/Sem4 development by creating an account on GitHub.

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Sixth Edition We also provide zip files of the all Powerpoint files, PDF files, and all figures used in the text are authorized for personal use, and for use in conjunction with a course for which Database System Concepts is the prescribed text. We provide solutions to the Practice Exercises of the Sixth Edition of Database System Concepts, by Silberschatz, Korth and Sudarshan. for use in conjunction with a course for which Database System Concepts is the prescribed text. pdf. Database Concepts 6th Edition By David M Kroenke And J Auer database concepts 6th edition by database system concepts sixth edition avi silberschatz.

The exercises are divided into two sets: practice exercises and exercises. The solutions for the practice exercises are publicly available on the Web site of the book. Students are encouraged to solve the practice exercises on their own, and later use the solutions on the Web site to check their own solutions.

Many chapters have a tools section at the end of the chapter that provides information on software tools related to the topic of the chapter; some of these tools can be used for laboratory exercises.

SQL DDL and sample data for the university database and other relations used in the exercises are available on the Web site of the book, and can be used for laboratory exercises. These sections may be omitted if so desired, without a loss of continuity.

It is possible to design courses by using various subsets of the chapters. Some of the chapters can also be covered in an order different from their order in the book. We expect most courses will cover at least Section 5.

Alternatively, this chapter may be omitted from an introductory course.

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We recommend covering Section 6. However, Sections 6.

You might choose to use Chapters 14 and 17, while omitting Chapters 15, 16, 18 and 19, if you defer these latter chapters to an advanced course. Alternatively, they can be used as an illustration of concepts when the earlier chapters are presented in class.

Model course syllabi, based on the text, can be found on the Web site of the book. Answers to the practice exercises.

The five appendices. An up-to-date errata list. Laboratory material, including SQL DDL and sample data for the university schema and other relations used in exercises, and instructions for setting up and using various database systems and tools.

We would appreciate it if you would notify us of any errors or omissions in the book that are not on the current list of errata. We would be glad to receive suggestions on improvements to the book. We also welcome any contributions to the book Web site that could be of use to other readers, such as programming exercises, project suggestions, online labs and tutorials, and teaching tips.

Acknowledgments Many people have helped us with this sixth edition, as well as with the previous five editions from which it is derived. Sarda for feedback that helped us improve several chapters, in particular Chapter 11; Vikram Pudi for motivating us to replace the earlier bank schema; and Shetal Shah for feedback on several chapters.

Lu, Alex N. Napitupulu, H. Kaplan, Graham J. The developmental editor was Melinda D. The project manager was Melissa Leick.

The marketing manager was xxvi Preface Curt Reynolds. The production supervisor was Laura Fuller. The book designer was Brenda Rolwes. The cover designer was Studio Montage, St. Louis, Missouri. The copyeditor was George Watson. The proofreader was Kevin Campbell. The freelance indexer was Tobiah Waldron. The Aptara team consisted of Raman Arora and Sudeshna Nandy Personal Notes Sudarshan would like to acknowledge his wife, Sita, for her love and support, and children Madhur and Advaith for their love and joie de vivre.

Hank would like to acknowledge his wife, Joan, and his children, Abby and Joe, for their love and understanding. Avi would like to acknowledge Valerie for her love, patience, and support during the revision of this book. The collection of data, usually referred to as the database, contains information relevant to an enterprise.

The primary goal of a DBMS is to provide a way to store and retrieve database information that is both convenient and efficient. Database systems are designed to manage large bodies of information.

Management of data involves both defining structures for storage of information and providing mechanisms for the manipulation of information. In addition, the database system must ensure the safety of the information stored, despite system crashes or attempts at unauthorized access.

If data are to be shared among several users, the system must avoid possible anomalous results. Because information is so important in most organizations, computer scientists have developed a large body of concepts and techniques for managing data.

These concepts and techniques form the focus of this book. This chapter briefly introduces the principles of database systems. Apago PDF Enhancer 1. Airlines were among the first to use databases in a geographically distributed manner. As the list illustrates, databases form an essential part of every enterprise today, storing not only types of information that are common to most enterprises, but also information that is specific to the category of the enterprise.

Over the course of the last four decades of the twentieth century, use of databases grew in all enterprises. In the early days, very few people interacted directly with database systems, although without realizing it, they interacted with databases indirectly—through printed reports such as credit card statements, or through agents such as bank tellers and airline reservation agents.

Then automated teller machines came along and let users interact directly with databases. The Internet revolution of the late s sharply increased direct user access to databases.

Organizations converted many of their phone interfaces to databases into Web interfaces, and made a variety of services and information available online. For instance, when you access an online bookstore and browse a book or music collection, you are accessing data stored in a database.

When you enter an order online, your order is stored in a database. When you access a Web site, informa- 1. Furthermore, data about your Web accesses may be stored in a database. The importance of database systems can be judged in another way—today, database system vendors like Oracle are among the largest software companies in the world, and database systems form an important part of the product line of Microsoft and IBM.

As an example of such methods, typical of the s, consider part of a university organization that, among other data, keeps information about all instructors, students, departments, and course offerings. One way to keep the information on a computer is to store it in operating system files. New application programs are added to the system as the need arises. For example, suppose that a university decides to create a new major say, computer science.

As a result, the university creates a new department and creates new permanent files or adds information to existing files to record information about all the instructors in the department, students in that major, course offerings, degree requirements, etc. The university may have to write new application programs to deal with rules specific to the new major. New application programs may also have to be written to handle new rules in the university. Thus, as time goes by, the system acquires more files and more application programs.

This typical file-processing system is supported by a conventional operating system. The system stores permanent records in various files, and it needs different application programs to extract records from, and add records to, the appropriate files. Before database management systems DBMSs were introduced, organizations usually stored information in such systems. Since different programmers create the files and application programs over a long period, the various files are likely to have different structures and the programs may be written in several programming languages.

Moreover, the same information may be duplicated in several places files. For example, if a student has a double major say, music and mathematics the address and telephone number of that student may appear in a file that consists of student records of students in the Music department and in a file that consists of student records of students in the Mathematics department. This redundancy leads to higher storage and access cost. In addition, it may lead to data inconsistency; that is, the various copies of the same data may no longer agree.

Solutions to Practice Exercises

For example, a changed student address may be reflected in the Music department records but not elsewhere in the system. Suppose that one of the university clerks needs to find out the names of all students who live within a particular postal-code area.

The clerk asks the data-processing department to generate such a list. Because the designers of the original system did not anticipate this request, there is no application program on hand to meet it.

There is, however, an application program to generate the list of all students. The university clerk has now two choices: either obtain the list of all students and extract the needed information manually or ask a programmer to write the necessary application program.

Both alternatives are obviously unsatisfactory. Suppose that such a program is written, and that, several days later, the same clerk needs to trim that list to include only those students who have taken at least 60 credit hours. As expected, a program to generate such a list does not exist. Again, the clerk has the preceding two options, neither of which is satisfactory.

The point here is that conventional file-processing environments do not allow needed data to be retrieved in a convenient and efficient manner. More responsive data-retrieval systems are required for general use. Because data are scattered in various files, and files may be in different formats, writing new application programs to retrieve the appropriate data is difficult.

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The data values stored in the database must satisfy certain types of consistency constraints. Suppose the university maintains an account for each department, and records the balance amount in each account. Suppose also that the university requires that the account balance of a department may never fall below zero. Developers enforce these constraints in the system by adding appropriate code in the various application programs.

However, when new constraints are added, it is difficult to change the programs to enforce them.

The problem is compounded when constraints involve several data items from different files. A computer system, like any other device, is subject to failure.

In many applications, it is crucial that, if a failure occurs, the data 1. Clearly, it is essential to database consistency that either both the credit and debit occur, or that neither occur.

That is, the funds transfer must be atomic—it must happen in its entirety or not at all. It is difficult to ensure atomicity in a conventional file-processing system. For the sake of overall performance of the system and faster response, many systems allow multiple users to update the data simultaneously.

Indeed, today, the largest Internet retailers may have millions of accesses per day to their data by shoppers. In such an environment, interaction of concurrent updates is possible and may result in inconsistent data. Suppose that the programs executing on behalf of each withdrawal read the old balance, reduce that value by the amount being withdrawn, and write the result back. To guard against this possibility, the system must maintain some form of supervision.

But supervision is difficult to provide because data may be accessed by many different application programs that have not been coordinated previously. As another example, suppose a registration program maintains a count of students registered for a course, in order to enforce limits on the number of students registered. When a student registers, the program reads the current count for the courses, verifies that the count is not already at the limit, adds one to the count, and stores the count back in the database.

Suppose two students register concurrently, with the count at say The two program executions may both read the value 39, and both would then write back 40, leading to an incorrect increase of only 1, even though two students successfully registered for the course and the count should be Furthermore, suppose the course registration limit was 40; in the above case both students would be able to register, leading to a violation of the limit of 40 students.

Not every user of the database system should be able to access all the data. For example, in a university, payroll personnel need to see only that part of the database that has financial information. They do not need access to information about academic records. Show an example of this relation for two students, one of whom has three siblings and the other of whom has only two siblings.

List the candidate keys in this relation. State the functional dependencies in this relation. Explain why this relation does not meet the relational design criteria set out in this chapter i. Some attributes are functionally dependent on a part of the composite primary key. Page 16 of 35 Full file at https: Divide this relation into a set of relations that meet the relational design criteria that is, that are well formed. StudentNumber, SiblingName Is every determinant a candidate key?

StudentNumber Is every determinant a candidate key? In this case, the relational structure is: Show an example of this relation for two students, one of whom has three siblings and the other of whom has one sibling. Assume that each student has a single major. Page 17 of 35 Full file at https: Show the data changes necessary to add a second major for only the first student.

Based on your answer to part B, show the data changes necessary to add a second major for the second student. Explain the differences in your answers to parts B and C. Comment on the desirability of this situation. We had to add three rows in the first case—one major for each of the siblings of the student. This is nuts!

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Divide this relation into a set of well-formed relations. StudentNumber, Major Is every determinant a candidate key? Page 19 of 35 Full file at https: In a properly normalized relation, each row of the relation consists of a primary key value which is a determinant and attribute values which are all functionally dependent on the primary key. Thus, properly normalized relations store instances of functional dependencies, and only instances of functional dependencies.

So we can say that the purpose of relations is to store instances of functional dependencies. Figure shows data that Regional Labs collects about projects and the employees assigned to them. Assuming that all functional dependencies are apparent in this data, which of the following are true? Are all the nonkey attributes if any dependent on the primary key?

To give an employee a salary, we must first assign the employee to a project. If we change a Salary, we have to change it in multiple places and may create inconsistent data. Is ProjectID a determinant? If so, based on which functional dependencies in part A? Is EmployeeName a determinant? Is EmployeeSalary a determinant? NO Actually, for the data in Figure , it is a determinant.

However, the dataset is too small to validate this determinant, and logically EmployeeSalary is not a determinant! Does this relation contain a transitive dependency? If so, what is it? Redesign the relation to eliminate modification anomalies.

The following seems workable: Garden Glory is owned by two partners. They employ two office administrators and a number of full- and part-time gardeners. Garden Glory will provide one-time garden services, but it specializes in ongoing service and maintenance. Many of its customers have multiple buildings, apartments, and rental houses that require gardening and lawn maintenance services.

Figure shows data that Garden Glory collects about properties and services. Using these data, state assumptions about functional dependencies among the columns of data. Justify your assumptions on the basis of these sample data and also on the basis of what you know about service businesses. From the data it appears that there are many functional dependencies that could be defined. Some examples are: Page 22 of 35 Full file at https: There is simply not enough data to reply on it.

Logically, it seems that we need one ID column—a surrogate key will be required here. With regard to services, it would seem likely that a given service could be given to the same property, but on different dates. So, if we had a good determinant for property, then the last functional dependency would be true.

So, the following seems workable: Given your assumptions in part A, comment on the appropriateness of the following designs: For example, PropertyName does not determine ServiceDate.

There may be more than one service on a given date. For example, PropertyName, ServiceDate does not determine Description since there may be more than one service at a property on a given date. For example, PropertyID, ServiceDate does not determine Description since there may be more than one service at a property on a given.

Page 23 of 35 Full file at https: The question then becomes: Which one should we keep? Finally the relationship is set up correctly. Now we can have many services even on the same date for one property. Suppose Garden Glory decides to add the following table: Modify the tables from part B as necessary to minimize the amount of data duplication. Will this design work for the data in Figure ?

If not, modify the design so that this data will work. State the assumptions implied by this design. Page 24 of 35 Full file at https: This also means that a service can be applied to multiple, but different, properties on the same date. However, a property may not have the same service on the same date. All of this seems reasonable.

Now we need to check with the users. While James River Jewelry does sell typical jewelry downloadd form jewelry vendors, including such items as rings, necklaces, earrings, and watches, it specializes in hard-to-find Asian jewelry. It has a small but loyal clientele, and it wants to further increase customer loyalty by creating a frequent downloader program.

In this program, after every 10 downloads, a customer will receive a credit equal to 50 percent of the sum of his or her 10 most recent downloads. Figure D-1 shows data that James River Jewelry collects for its frequent downloader program. Page 25 of 35 Full file at https: Justify your assumptions on the basis of these sample data and also on the basis of what you know about retail sales.

From the data it would appear: For example, name is not a good determinant in a retail application; there may be many customers with the same name. Another functional dependency is: For example, Name does not determine InvoiceNumber because one customer may have made more than one download.

And why is InvoiceNumber the key for data about a customer? Page 26 of 35 Full file at https: Given a unique Email address, Email works as a key for customer data. Unfortunately, Email does not determine InvoiceNumber and therefore is not a sufficient key.

A unique ID column is a good idea, and works as a key for customer data. The design breaks up the themes and has a proper foreign key. However, the use of Email as a primary key may be a problem if two customers share an Email address. However, why was Phone moved?

Modify what you consider to be the best design in part B to include a column called AwarddownloadAmount. Assume that returns will be recorded with invoices having a negative PreTaxAmount. The result is: Assume that the new table will hold data concerning the date and amount of an award that is given after a customer has downloadd 10 items. Ensure that your new table has appropriate primary and foreign keys. The new table is: For example, the store sells antique dining room tables and new tablecloths.

The antiques are downloadd from both individuals and wholesalers, and the new items are downloadd from distributors. The antiques are unique, although some multiple items, such as dining room chairs, may be available as a set sets are never broken. The new items are not unique, and an item may be reordered if it is out of stock. New items are also available in various sizes and colors for example, a particular style of tablecloth may be available in several sizes and in a variety of colors.

Page 28 of 35 Full file at https: Page 29 of 35 Full file at https: From the sample sales data it would appear: The one trustable functional dependency here is: For example, Item is not a good determinant in a retail application; there may be many Items with the same designator.

The most trustworthy functional dependencies here are: There may be many customers with the same last name. Page 30 of 35 Full file at https: There may be many customers with the same last name and first name.

Phone will be fairly unique, and combined with FirstName may overcome the problem of two people with the same phone number being in the customer list but not necessarily—what if three students are sharing an apartment, and two of them are Bill Smith and Bill Jones? However, this will not determine the download information of download date, etc. There may be many customers with the same last name and first name, and some of them may make a download on the same date.

The only good thing about this is that it is starting to address the download information. If LastName, FirstName was unique, then customers would be limited to one download per day. Same objections as above in 4, except that now customers would be limited to one of a particular item per day. For example, a customer could not download two antique chairs on the same day! Finally the customer and download data is effectively broken up, but there is no foreign key to link the two tables.

But everything else that was wrong in design 6 above is still a problem.Multidatabases, which were earlier in the advanced transaction processing chapter, are now covered earlier as part of the distributed database chapter. Apago PDF Enhancer All topics not listed above are updated from the fifth edition, though their overall organization is relatively unchanged. In the early days, very few people interacted directly with database systems, although without realizing it, they interacted with databases indirectly—through printed reports such as credit card statements, or through agents such as bank tellers and airline reservation agents.

Now we can have many services even on the same date for one property. Schema design issues are deferred to Part 2.