1 Database Management System DATABASE MANAGEMENT SYSTEMS Subject Cde: I.A. Marks: 25 Hurs/Week: 04 Exam Hurs: 03 Ttal Hu...
Database Management System
10CS54
DATABASE MANAGEMENT SYSTEMS Subject Code: 10CS54
I.A. Marks: 25
Hours/Week: 04
Exam Hours: 03
Total Hours: 52
Exam Marks: 100
PART - A UNIT – 1
6 Hours
Introduction: Introduction; An example; Characteristics of Database approach; Actors on the screen; Workers behind the scene; Advantages of using DBMS approach; A brief history of database applications; when not to use a DBMS. Data models, schemas and instances; Three-schema architecture and data independence; Database languages and interfaces; the database system environment; Centralized and client-server architectures; Classification of Database Management systems. UNIT – 2
6 Hours
Entity-Relationship Model: Using High-Level Conceptual Data Models for Database Design; An Example Database Application; Entity Types, Entity Sets, Attributes and Keys; Relationship types, Relationship Sets, Roles and Structural Constraints; Weak Entity Types; Refining the ER Design; ER Diagrams, Naming Conventions and Design Issues; Relationship types of degree higher than two. UNIT – 3
8 Hours
Relational Model and Relational Algebra: Relational Model Concepts; Relational Model Constraints and Relational Database Schemas; Update Operations, Transactions and dealing with constraint violations; Unary Relational Operations: SELECT and PROJECT; Relational Algebra Operations from Set Theory; Binary Relational Operations: JOIN and DIVISION; Additional Relational Operations; Examples of Queries in Relational Algebra; Relational Database Design Using ER- to-Relational Mapping.
Dept. of ISE, SJBIT
Database Management System
10CS54
UNIT – 4
6 Hours
SQL – 1: SQL Data Definition and Data Types; Specifying basic constraints in SQL; Schema change statements in SQL; Basic queries in SQL; More complex SQL Queries.
PART - B UNIT – 5
6 Hours
SQL – 2 : Insert, Delete and Update statements in SQL; Specifying constraints as Assertion and Trigger; Views (Virtual Tables) in SQL; Additional features of SQL; Database programming issues and techniques; Embedded SQL, Dynamic SQL; Database stored procedures and SQL /PSM.
UNIT – 6
6 Hours
Database Design – 1: Informal Design Guidelines for Relation Schemas; Functional ependencies; Normal Forms Based on Primary Keys; General Definitions of Second and Third Normal Forms; Boyce-Codd Normal Form
UNIT – 7
6 Hours
Database Design -2: Properties of Relational Decompositions; Algorithms for Relational Database Schema Design; Multivalued Dependencies and Fourth Normal Form; Join Dependencies and Fifth Normal Form; Inclusion Dependencies; Other Dependencies and Normal Forms UNIT – 8
8 Hours
Transaction Management: The ACID Properties; Transactions and Schedules; Concurrent Execution of Transactions; Lock- Based Concurrency Control; Performance of locking; Transaction support in SQL; Introduction to crash recovery; 2PL, Serializability and Recoverability; Lock Management; Introduction to ARIES; The log; Other recovery-related structures; The write-ahead log protocol; Check pointing; Recovering from a System Crash; Media Recovery; Other approaches and interaction with concurrency control.
Dept. of ISE, SJBIT
Database Management System
10CS54
Text Books: 1. Elmasri and Navathe: Fundamentals of Database Systems,5th Edition, Pearson Education, 2007.(Chapters 1, 2, 3 except 3.8, 5, 6.1 to 6.5, 7.1, 8, 9.1, 9.2 except SQLJ, 9.4, 10) 2. Raghu Ramakrishnan and Johannes Gehrke: Database Management Systems, 3rd Edition, McGraw-Hill, 2003. (Chapters 16, 17.1, 17.2, 18)
Reference Books: 1. Silberschatz, Korth and Sudharshan: Data base System Concepts, 6th Edition, McGrawHill, 2010. 2. C.J. Date, A. Kannan, S. Swamynatham: An Introduction to Database Systems, 8th Edition, Pearson Education, 2006
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Database Management System
10CS54
INDEX SHEET Contents
Page No
Unit 1 Databases and Database Users
1-13
1.1 Introduction 1.2: An Example: 1.3: Characteristics of the Database Approach: 1.4: Actors on the Scene 1.5: Workers behind the Scene 1.6: Capabilities/Advantages of DBMS's 1.7: A Brief History of Database Applications 1.8: When Not to Use a DBMS Unit 2 ENTITY.1 Data Models, Schemas, and Instances 2.1-RELATIONSHIP MODEL 2.1.2: Schemas, Instances, and Database State 2.2 DBMS Architecture and Data Independence 2.2.1: Three-Schema Architecture 2.3 Database Languages and Interfaces 2.3.1 DBMS Languages 2.3.2 DBMS Interfaces 2.4 Database System Environment 2.5 Centralized and Client/Server Architectures for DBMS's 2.6 Classification of DBMS's 2.7. Modeling Using the Entity-Relationship Model 2.8 Entity-Relationship (ER) Model 2.8.1 Entities and Attributes 2.8.2: Entity Types, Entity Sets, Keys, and Domains 2.8.3 Initial Conceptual Design of COMPANY database Dept. of ISE, SJBIT
14-30
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2.9 Relationship Types, Sets, Roles, and Structural Constraints 2.9.1: Ordering of entity types in relationship types 2.9.2 Degree of a relationship type 2.9.3 Constraints on Relationship Types 2.9.4 Attributes of Relationship Types 2.10 Weak Entity Types UNIT 3 The Relational Data Model and Relational Database Constraints and Relational Algebra 31-49 3.1 Relational Model Concepts 3.1.2 Characteristics of Relations 3.1.3 Relational Model Notation 3.2 Relational Model Constraints and Relational Database Schemas 3.2.1 Domain Constraints 3.2.2 Key Constraints 3.2.3 Relational Databases and Relational Database Schemas 3.2.4 Entity Integrity, Referential Integrity, and Foreign Keys 3.3 Update Operations and Dealing with Constraint Violations 3.3.1 Insert 3.3.2 Delete 3.3.3 Update: 3.3.4 Transactions and dealing with constraints 3.4 Relational Operation 3.5 Relational algebra operation Set theory Operations 3.6 JOIN Operations 3.7 Additional Relational Operations 3.8 Examples of Queries in Relational Algebra 3.9 Relational Database Design Using ER-to-Relational Mapping
Dept. of ISE, SJBIT
Database Management System
UNIT 4 SQL The Relational Database Standard
10CS54
50-60
4.1 Data Definition, Constraints, and Schema Changes in SQL2 4.2 Basic Queries in SQL 4.3 More Complex SQL Queries UNIT 5 SQL The Relational Database Standard
61-70
5.1 Update Statements in SQL 5.2 Views in SQL 5.3 Additional features 5.4 Database Programming 5.5 Embedded SQL 5.6 Dynamic SQL 5.7 Database stored procedures and SQL/PSM UNIT-6 Data Base design-1
71-84
6.1 Informal design guidelines for relation schemas 6.1.1 Semantics of relations attributes 6.2. Inference Rules 6.3 Normalization 6.3.1 First Normal Form (1NF) 6.3.2 Second Normal Form (2NF) 6.3.3 Third Normal Form (3NF) 6.4 Boyce-Codd Normal Form (BCNF) UNIT-7 Data base design 2 7.1 Properties of relational decomposition 7.2 Algorithms for Relational Database Schema Design 7.2.1 Decomposition and Dependency Preservation 7.2.2 Lossless-join Dependency 7.3 Multivolume Dependencies and Fourth Normal Form (4NF) Dept. of ISE, SJBIT
85-92
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7.3.1 Fourth Normal Form (4NF) 7.4 Join Dependencies and 5 NF 7.5 Other dependencies: 7.5.1 Template Dependencies 7.5.2 Domain Key Normal Form UNIT 8 Transaction Processing Concepts 8.1 Introduction to Transaction Processing 8.2 Transactions, Read and Write Operations 8.3 Why Concurrency Control Is Needed 8.4 Why Recovery Is Needed 8.5 Transaction and System Concepts 8.6 The System Log 8.7 Desirable Properties of Transactions 8.8 Schedules and Recoverability
Dept. of ISE, SJBIT
93-106
Data Base Management System
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UNIT 1
Databases and Database Users 1.1 Introduction 1.2: An Example: 1.3: Characteristics of the Database Approach: 1.4: Actors on the Scene 1.5: Workers Behind the Scene 1.6: Capabilities/Advantages of DBMS's 1.7: A Brief History of Database Applications 1.8: When Not to Use a DBMS
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Unit 1 Databases and Database Users 1.1 Introduction Importance: Database systems have become an essential component of life in modern society, in that many frequently occurring events trigger the accessing of at least one database: bibliographic library searches, bank transactions, hotel/airline reservations, grocery store purchases, online (Web) purchases, etc., etc. Traditional vs. more recent applications of databases: The applications mentioned above are all "traditional" ones for which the use of rigidlystructured textual and numeric data suffices. Recent advances have led to the application of database technology to a wider class of data. Examples include multimedia databases (involving pictures, video clips, and sound messages) and geographic databases (involving maps, satellite images). Also, database search techniques are applied by some WWW search engines. Definitions The term database is often used, rather loosely, to refer to just about any collection of related data. E&N say that, in addition to being a collection of related data, a database must have the following properties:
It represents some aspect of the real (or an imagined) world, called the miniworld or universe of discourse. Changes to the miniworld are reflected in the database. Imagine, for example, a UNIVERSITY miniworld concerned with students, courses, course sections, grades, and course prerequisites.
It is a logically coherent collection of data, to which some meaning can be attached. (Logical coherency requires, in part, that the database not be self-contradictory.)
It has a purpose: there is an intended group of users and some preconceived applications that the users are interested in employing.
To summarize: a database has some source (i.e., the miniworld) from which data are derived, some degree of interaction with events in the represented miniworld (at least insofar as the data is updated when the state of the miniworld changes), and an audience that is interested in using it.
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An Aside: data vs. information vs. knowledge: Data is the representation of "facts" or "observations" whereas information refers to the meaning thereof (according to some interpretation). Knowledge, on the other hand, refers to the ability to use information to achieve intended ends. Computerized vs. manual: Not surprisingly (this being a CS course), our concern will be with computerized database systems, as opposed to manual ones, such as the card catalog-based systems that were used in libraries in ancient times (i.e., before the year 2000). (Some authors wouldn't even recognize a non-computerized collection of data as a database, but E&N do.) Size/Complexity: Databases run the range from being small/simple (e.g., one person's recipe database) to being huge/complex (e.g., Amazon's database that keeps track of all its products, customers, and suppliers). Definition: A database management system (DBMS) is a collection of programs enabling users to create and maintain a database. More specifically, a DBMS is a general purpose software system facilitating each of the following (with respect to a database):
definition: specifying data types (and other constraints to which the data must conform) and data organization
construction: the process of storing the data on some medium (e.g., magnetic disk) that is controlled by the DBMS
manipulation: querying, updating, report generation
sharing: allowing multiple users and programs to access the database "simultaneously"
system protection: preventing database from becoming corrupted when hardware or software failures occur
security protection: preventing unauthorized or malicious access to database.
Given all its responsibilities, it is not surprising that a typical DBMS is a complex piece of software. A database together with the DBMS software is referred to as a database system. (See Figure 1.1, page 7.) 1.2: An Example: UNIVERSITY database in Figure 1.2. Notice that it is relational!
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Among the main ideas illustrated in this example is that each file/relation/table has a set of named fields/attributes/columns, each of which is specified to be of some data type. (In addition to a data type, we might put further restrictions upon a field, e.g., GRADE_REPORT must have a value from the set {'A', 'B', ..., 'F'}.) The idea is that, of course, each table will be populated with data in the form of records/tuples/rows, each of which represents some entity (in the miniworld) or some relationship between entities. For example, each record in the STUDENT table represents a —surprise!— student. Similarly for the COURSE and SECTION tables. On the other hand, each record in GRADE_REPORT represents a relationship between a student and a section of a course. And each record in PREREQUISITE represents a relationship between two courses. Database manipulation involves querying and updating. Examples of (informal) queries:
Retrieve the transcript(s) of student(s) named 'Smith'.
List the names of students who were enrolled in a section of the 'Database' course in Spring 2006, as well as their grades in that course section.
List all prerequisites of the 'Database' course.
Examples of (informal) updates:
Change the CLASS value of 'Smith' to sophom*ore (i.e., 2).
Insert a record for a section of 'File Processing' for this semester.
Remove from the prerequisites of course 'CMPS 340' the course 'CMPS 144'.
Of course, a query/update must be conveyed to the DBMS in a precise way (via the query language of the DBMS) in order to be processed. As with software in general, developing a new database (or a new application for an existing database) proceeds in phases, including requirements analysis and various levels of design (conceptual (e.g., Entity-Relationship Modeling), logical (e.g., relational), and physical (file structures)). 1.3: Characteristics of the Database Approach: Database approach vs. File Processing approach: Consider an organization/enterprise that is organized as a collection of departments/offices. Each department has certain data processing
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"needs", many of which are unique to it. In the file processing approach, each department would control a collection of relevant data files and software applications to manipulate that data. For example, a university's Registrar's Office would maintain data (and programs) relevant to student grades and course enrollments. The Bursar's Office would maintain data (and programs) pertaining to fees owed by students for tuition, room and board, etc. (Most likely, the people in these offices would not be in direct possession of their data and programs, but rather the university's Information Technology Department would be responsible for providing services such as data storage, report generation, and programming.) One result of this approach is, typically, data redundancy, which not only wastes storage space but also makes it more difficult to keep changing data items consistent with one another, as a change to one copy of a data item must be made to all of them (called duplication-of-effort). Inconsistency results when one (or more) copies of a datum are changed but not others. (E.g., If you change your address, informing the Registrar's Office should suffice to ensure that your grades are sent to the right place, but does not guarantee that your next bill will be, as the copy of your address "owned" by the Bursar's Office might not have been changed.) In the database approach, a single repository of data is maintained that is used by all the departments in the organization. (Note that "single repository" is used in the logical sense. In physical terms, the data may be distributed among various sites, and possibly mirrored.) Main Characteristics of database approach: 1. Self-Description: A database system includes —in addition to the data stored that is of relevance to the organization— a complete definition/description of the database's structure and constraints. This meta-data (i.e., data about data) is stored in the so-called system catalog, which contains a description of the structure of each file, the type and storage format of each field, and the various constraints on the data (i.e., conditions that the data must satisfy). See Figures 1.1 and 1.3. The system catalog is used not only by users (e.g., who need to know the names of tables and attributes, and sometimes data type information and other things), but also by the DBMS software, which certainly needs to "know" how the data is structured/organized in order to interpret it in a manner consistent with that structure. Recall that a DBMS is
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general purpose, as opposed to being a specific database application. Hence, the structure of the data cannot be "hard-coded" in its programs (such as is the case in typical file processing approaches), but rather must be treated as a "parameter" in some sense. 2. Insulation between Programs and Data; Data Abstraction: Program-Data Independence: In traditional file processing, the structure of the data files accessed by an application is "hard-coded" in its source code. (E.g., Consider a file descriptor in a COBOL program: it gives a detailed description of the layout of the records in a file by describing, for each field, how many bytes it occupies.) If, for some reason, we decide to change the structure of the data (e.g., by adding the first two digits to the YEAR field, in order to make the program Y2K compliant!), every application in which a description of that file's structure is hard-coded must be changed! In contrast, DBMS access programs, in most cases, do not require such changes, because the structure of the data is described (in the system catalog) separately from the programs that access it and those programs consult the catalog in order to ascertain the structure of the data (i.e., providing a means by which to determine boundaries between records and between fields within records) so that they interpret that data properly. See Figure 1.4. In other words, the DBMS provides a conceptual or logical view of the data to application programs, so that the underlying implementation may be changed without the programs being modified. (This is referred to as program-data independence.) Also, which access paths (e.g., indexes) exist are listed in the catalog, helping the DBMS to determine the most efficient way to search for items in response to a q uery. Data Abstraction: A data model is used to hide storage details and present the users with a conceptual view of the database. Programs refer to the data model constructs rather than data storage details Note: In fairness to COBOL, it should be pointed out that it has a COPY feature that allows different application programs to make use of the same file descriptor stored in a "library". This provides some degree of program-data independence, but not nearly as much as a good DBMS does. End of note.
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Example by which to illustrate this concept: Suppose that you are given the task of developing a program that displays the contents of a particular data file. Specifically, each record should be displayed as follows:
Record #i: value of first field value of second field ... ... value of last field To keep things very simple, suppose that the file in question has fixed-length records of 57 bytes with six fixed-length fields of lengths 12, 4, 17, 2, 15, and 7 bytes, respectively, all of which are ASCII strings. Developing such a program would not be difficult. However, the obvious solution would be tailored specifically for a file having the particular structure described here and would be of no use for a file with a different structure. Now suppose that the problem is generalized to say that the program you are to develop must be able to display any file having fixed-length records with fixed-length fields that are ASCII strings. Impossible, you say? Well, yes, unless the program has the ability to access a description of the file's structure (i.e., lengths of its records and the fields therein), in which case the problem is not hard at all. This illustrates the power of metadata, i.e., data describing other data. 3. Multiple Views of Data: Different users (e.g., in different departments of an organization) have different "views" or perspectives on the database. For example, from the point of view of a Bursar's Office employee, student data does not include anything about which courses were taken or which grades were earned. (This is an example of a subset view.) As another example, a Registrar's Office employee might think that GPA is a field of data in each student's record. In reality, the underlying database might calculate that value each time it is needed. This is called virtual (or derived) data.
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A view designed for an academic advisor might give the appearance that the data is structured to point out the prerequisites of each course. (See Figure 1.5, page 14.) A good DBMS has facilities for defining multiple views. This is not only convenient for users, but also addresses security issues of data access. (E.g., The Registrar's Office view should not provide any means to access financial data.) 4. Data Sharing and Multi-user Transaction Processing: As you learned about (or will) in the OS course, the simultaneous access of computer resources by multiple users/processes is a major source of complexity. The same is true for multi-user DBMS's. Arising from this is the need for concurrency control, which is supposed to ensure that several users trying to update the same data do so in a "controlled" manner so that the results of the updates are as though they were done in some sequential order (rather than interleaved, which could result in data being incorrect). This gives rise to the concept of a transaction, which is a process that makes one or more accesses to a database and which must have the appearance of executing in isolation from all other transactions (even ones that access the same data at the "same time") and of being atomic (in the sense that, if the system crashes in the middle of its execution, the database contents must be as though it did not execute at all). Applications such as airline reservation systems are known as online transaction processing applications.
1.4: Actors on the Scene These apply to "large" databases, not "personal" databases that are defined, constructed, and used by a single person via, say, Microsoft Access. Users may be divided into Those who actually use and control the database content, and those who design, develop and maintain database applications (called “Actors on the Scene”), and Those who design and develop the DBMS software and related tools, and the computer systems operators (called “Workers Behind the Scene”).
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1. Database Administrator (DBA): This is the chief administrator, who oversees and manages the database system (including the data and software). Duties include authorizing users to access the database, coordinating/monitoring its use, acquiring hardware/software for upgrades, etc. In large organizations, the DBA might have a support staff. 2. Database Designers: They are responsible for identifying the data to be stored and for choosing an appropriate way to organize it. They also define views for different categories of users. The final design must be able to support the requirements of all the user sub-groups. 3. End Users: These are persons who access the database for querying, updating, and report generation. They are main reason for database's existence! o
Casual end users: use database occasionally, needing different information each time; use query language to specify their requests; typically middle- or high-level managers.
o
Naive/Parametric end users: Typically the biggest group of users; frequently query/update the database using standard canned transactions that have been carefully programmed and tested in advance. Examples:
bank tellers check account balances, post withdrawals/deposits
reservation clerks for airlines, hotels, etc., check availability of seats/rooms and make reservations.
shipping clerks (e.g., at UPS) who use buttons, bar code scanners, etc., to update status of in-transit packages.
o
Sophisticated end users: engineers, scientists, business analysts who implement their own applications to meet their complex needs.
o
Stand-alone users: Use "personal" databases, possibly employing a specialpurpose (e.g., financial) software package. Mostly maintain personal databases using ready-to-use packaged applications.
o
An example is a tax program user that creates its own internal database.
o
Another example is maintaining an address book
4. System Analysts, Application Programmers, Software Engineers:
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System Analysts: determine needs of end users, especially naive and parametric users, and develop specifications for canned transactions that meet these needs.
o
Application Programmers: Implement, test, document, and maintain programs that satisfy the specifications mentioned above.
1.5: Workers Behind the Scene
DBMS system designers/implementors: provide the DBMS software that is at the foundation of all this!
Tool developers: design and implement software tools facilitating database system design, performance monitoring, creation of graphical user interfaces, prototyping, ets.
Operators and maintenance personnel: responsible for the day-to-day operation of the system.
1.6: Capabilities/Advantages of DBMS's 1. Controlling Redundancy: Data redundancy (such as tends to occur in the "file processing" approach) leads to wasted storage space, duplication of effort (when multiple copies of a datum need to be updated), and a higher liklihood of the introduction of inconsistency. On the other hand, redundancy can be used to improve performance of queries. Indexes, for example, are entirely redundant, but help the DBMS in processing queries more quickly. Another example of using redundancy to improve performance is to store an "extra" field in order to avoid the need to access other tables (as when doing a JOIN, for example). See Figure 1.6 (page 18): the StudentName and CourseNumber fields need not be there. A DBMS should provide the capability to automatically enforce the rule that no inconsistencies are introduced when data is updated. (Figure 1.6 again, in which Student_name does not match Student_number.) 2. Restricting Unauthorized Access: A DBMS should provide a security and authorization subsystem, which is used for specifying restrictions on user accounts. Common kinds of restrictions are to allow read-only access (no updating), or access only to a subset of the data (e.g., recall the Bursar's and Registrar's office examples from above).
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3. Providing Persistent Storage for Program Objects: Object-oriented database systems make it easier for complex runtime objects (e.g., lists, trees) to be saved in secondary storage so as to survive beyond program termination and to be retrievable at a later time. 4. Providing Storage Structures for Efficient Query Processing: The DBMS maintains indexes (typically in the form of trees and/or hash tables) that are utilized to improve the execution time of queries and updates. (The choice of which indexes to create and maintain is part of physical database design and tuning (see Chapter 16) and is the responsibility of the DBA. The query processing and optimization module is responsible for choosing an efficient query execution plan for each query submitted to the system. (See Chapter 15.) 5. Providing Backup and Recovery: The subsystem having this responsibility ensures that recovery is possible in the case of a system crash during execution of one or more transactions. 6. Providing Multiple User Interfaces: For example, query languages for casual users, programming language interfaces for application programmers, forms and/or command codes for parametric users, menu-driven interfaces for stand-alone users. 7. Representing Complex Relationships Among Data: A DBMS should have the capability to represent such relationships and to retrieve related data quickly. 8. Enforcing Integrity Constraints: Most database applications are such that the semantics (i.e., meaning) of the data require that it satisfy certain restrictions in order to make sense. Perhaps the most fundamental constraint on a data item is its data type, which specifies the universe of values from which its value may be drawn. (E.g., a Grade field could be defined to be of type Grade_Type, which, say, we have defined as including precisely the values in the set { "A", "A-", "B+", ..., "F" }. Another kind of constraint is referential integrity, which says that if the database includes an entity that refers to another one, the latter entity must exist in the database. For example, if (R56547, CIL102) is a tuple in the Enrolled_In relation, indicating that a student with ID R56547 is taking a course with ID CIL102, there must be a tuple in the Student relation corresponding to a student with that ID. 9. Permitting Inferencing and Actions Via Rules: In a deductive database system, one may specify declarative rules that allow the database to infer new data! E.g., Figure out
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which students are on academic probation. Such capabilities would take the place of application programs that would be used to ascertain such information otherwise. Active database systems go one step further by allowing "active rules" that can be used to initiate actions automatically. 1.7: A Brief History of Database Applications Early Database Applications: The Hierarchical and Network Models were introduced in mid 1960s and dominated during the seventies. A bulk of the worldwide database processing still occurs using these models. Relational Model based Systems: Relational model was originally introduced in 1970, was heavily researched and experimented with in IBM Research and several universities. Object-oriented and emerging applications: Object-Oriented Database Management Systems (OODBMSs) were introduced in late 1980s and early 1990s to cater to the need of complex data processing in CAD and other applications. Their use has not taken off much. Many relational DBMSs have incorporated object database concepts, leading to a new category called object-relational DBMSs (ORDBMSs) Extended relational systems add further capabilities (e.g. for multimedia data, XML, and other data types) Relational DBMS Products emerged in the 1980s Data on the Web and E-commerce Applications: Web contains data in HTML (Hypertext markup language) with links among pages. This has given rise to a new set of applications and E-commerce is using new standards like XML (eXtended Markup Language). Script programming languages such as PHP and JavaScript allow generation of dynamic Web pages that are partially generated from a database New functionality is being added to DBMSs in the following areas: Scientific Applications XML (eXtensible Markup Language)
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Image Storage and Management Audio and Video data management Data Warehousing and Data Mining Spatial data management Time Series and Historical Data Management The above gives rise to new research and development in incorporating new data types, complex data structures, new operations and storage and indexing schemes in database systems. Also allow database updates through Web pages 1.8: When Not to Use a DBMS Main inhibitors (costs) of using a DBMS: High initial investment and possible need for additional hardware. Overhead for providing generality, security, concurrency control, recovery, and integrity functions. When a DBMS may be unnecessary: If the database and applications are simple, well defined, and not expected to change. If there are stringent real-time requirements that may not be met because of DBMS overhead. If access to data by multiple users is not required. When no DBMS may suffice: If the database system is not able to handle the complexity of data because of modeling limitations If the database users need special operations not supported by the DBMS.
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UNIT 2
ENTITY.1 Data Models, Schemas, and Instances ENTITY.1 Data Models, Schemas, and Instances
2.1-RELATIONSHIP MODEL 2.1.2: Schemas, Instances, and Database State 2.2 DBMS Architecture and Data Independence 2.2.1: Three-Schema Architecture 2.3 Database Languages and Interfaces 2.3.1 DBMS Languages 2.3.2 DBMS Interfaces 2.4 Database System Environment 2.5 Centralized and Client/Server Architectures for DBMS's 2.6 Classification of DBMS's 2.7.Modeling Using the Entity-Relationship Model 2.8 Entity-Relationship (ER) Model 2.8.1 Entities and Attributes 2.8.2: Entity Types, Entity Sets, Keys, and Domains 2.8.3 Initial Conceptual Design of COMPANY database 2.9 Relationship Types, Sets, Roles, and Structural Constraints 2.9.1:Ordering of entity types in relationship types 2.9.2 Degree of a relationship type 2.9.3 Constraints on Relationship Types 2.9.4 Attributes of Relationship Types 2.10 Weak Entity Types
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UNIT 2 ENTITY-RELATIONSHIP MODEL 2.1 Data Models, Schemas, and Instances One fundamental characteristic of the database approach is that it provides some level of data abstraction by hiding details of data storage that are irrelevant to database users. A data model ---a collection of concepts that can be used to describe the conceptual/logical structure of a database--- provides the necessary means to achieve this abstraction. By structure is meant the data types, relationships, and constraints that should hold for the data. Most data models also include a set of basic operations for specifying retrievals/updates. Object-oriented data models include the idea of objects having behavior (i.e., applicable methods) being stored in the database (as opposed to purely "passive" data). According to C.J. Date (one of the leading database experts), a data model is an abstract, selfcontained, logical definition of the objects, operators, and so forth, that together constitute the abstract machine with which users interact. The objects allow us to model the structure of data; the operators allow us to model its behavior. In the relational data model, data is viewed as being organized in two-dimensional tables comprised of tuples of attribute values. This model has operations such as Project, Select, and Join. A data model is not to be confused with its implementation, which is a physical realization on a real machine of the components of the abstract machine that together constitute that model. Logical vs. physical!! There are other well-known data models that have been the basis for database systems. The bestknown models pre-dating the relational model are the hierarchical (in which the entity types form a tree) and the network (in which the entity types and relationships between them form a graph). Categories of Data Models (based on degree of abstractness):
high-level/conceptual: (e.g., ER model of Chapter 3) provides a view close to the way users would perceive data; uses concepts such as o
entity: real-world object or concept (e.g., student, employee, course, department, event)
o
attribute: some property of interest describing an entity (e.g., height, age, color)
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relationship: an interaction among entities (e.g., works-on relationship between an employee and a project)
Representational/implementational: intermediate level of abstractness; example is relational data model (or the network model alluded to earlier). Also called record-based model.
Low-level/physical: gives details as to how data is stored in computer system, such as record formats, orderings of records, access paths (indexes). (See Chapters 13-14.)
2.1.2: Schemas, Instances, and Database State One must distinguish between the description of a database and the database itself. The former is called the database schema, which is specified during design and is not expected to change often. (See Figure 2.1, p. 33, for schema diagram for relational UNIVERSITY database.) The actual data stored in the database probably changes often. The data in the database at a particular time is called the state of the database, or a snapshot. Application requirements change occasionally, which is one of the reasons why software maintenance is important. On such occasions, a change to a database's schema may be called for. An example would be to add a Date_of_Birth field/attribute to the STUDENT table. Making changes to a database schema is known as schema evolution. Most modern DBMS's support schema evolution operations that can be applied while a database is operational.
2.2 DBMS Architecture and Data Independence 2.2.1: Three-Schema Architecture: (See Figure 2.2, page 34.) This idea was first described by the ANSI/SPARC committee in late 1970's. The goal is to separate (i.e., insert layers of "insulation" between) user applications and the physical database. C.J. Date points out that it is an ideal that few, if any, real-life DBMS's achieve fully.
internal level: has an internal/physical schema that describes the physical storage structure of the database using a low-level data model)
Conceptual level: has a conceptual schema describing the (logical) structure of the whole database for a community of users. It hides physical storage details, concentrating upon describing entities, data types, relationships, user operations, and constraints. Can be described using either high-level or implementational data model.
External/view level: includes a number of external schemas (or user views), each of which describes part of the database that a particular category of users is interested in,
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hiding rest of database. Can be described using either high-level or implementational data model. (In practice, usually described using same model as is the conceptual schema.) Users (including application programs) submit queries that are expressed with respect to the external level. It is the responsibility of the DBMS to transform such a query into one that is expressed with respect to the internal level (and to transform the result, which is at the internal level, into its equivalent at the external level). Example: Select students with GPA > 3.5. Q: How is this accomplished? By virtue of mappings between the levels:
external/conceptual mapping (providing logical data independence)
conceptual/internal mapping (providing physical data independence)
Data independence is the capacity to change the schema at one level of the architecture without having to change the schema at the next higher level. We distinguish between logical and physical data independence according to which two adjacent levels are involved. The former refers to the ability to change the conceptual schema without changing the external schema. The latter refers to the ability to change the internal schema without having to change the conceptual. For an example of physical data independence, suppose that the internal schema is modified (because we decide to add a new index, or change the encoding scheme used in representing some field's value, or stipulate that some previously unordered file must be ordered by a particular field ). Then we can change the mapping between the conceptual and internal schemas in order to avoid changing the conceptual schema itself. Not surprisingly, the process of transforming data via mappings can be costly (performancewise), which is probably one reason that real-life DBMS's don't fully implement this 3-schema architecture.
2.3 Database Languages and Interfaces A DBMS supports a variety of users and must provide appropriate languages and interfaces for each category of users. DBMS Languages
DDL (Data Definition Language): used (by the DBA and/or database designers) to specify the conceptual schema.
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SDL (Storage Definition Language): used for specifying the internal schema
VDL (View Definition Language): used for specifying the external schemas (i.e., user views)
DML (Data Manipulation Language): used for performing operations such as retrieval and update upon the populated database
The above description represents some kind of ideal. In real-life, at least so far, the de facto standard DBMS language is SQL (Standard Query Language), which has constructs to support the functions needed by DDL, VDL, and DML languages. (Early versions of SQL had features in support of SDL functions, but no more.)
2.3.1 DBMS Languages Menu-based, forms-based, gui-based, natural language, special purpose for parametric users, for DBA. 2.3.2 DBMS Interfaces
Menu-based interfaces for web clients or browsing
Forms-based interfaces
GUI's
Natural Language Interfaces
Speech Input and Output
Interfaces for parametric users
Interfaces for the DBA
2.4 Database System Environment See Figure 2.3, page 41.
2.5 Centralized and Client/Server Architectures for DBMS's
2.6 Classification of DBMS's Based upon
underlying data model (e.g., relational, object, object-relational, network)
multi-user vs. single-user
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cost
general-purpose vs. special-purpose
types of access path options
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2.7 Data Modeling Using the Entity-Relationship Model Outline of Database Design The main phases of database design are depicted in Figure 3.1, page 59:
Requirements Collection and Analysis: purpose is to produce a description of the users' requirements.
Conceptual Design: purpose is to produce a conceptual schema for the database, including detailed descriptions of entity types, relationship types, and constraints. All these are expressed in terms provided by the data model being used. (Remark: As the ER model is focused on precisely these three concepts, it would seem that the authors are predisposed to using that data model!)
Implementation: purpose is to transform the conceptual schema (which is at a high/abstract level) into a (lower-level) representational/implementational model supported by whatever DBMS is to be used.
Physical Design: purpose is to decide upon the internal storage structures, access paths (indexes), etc., that will be used in realizing the representational model produced in previous phase.
2.8: Entity-Relationship (ER) Model Our focus now is on the second phase, conceptual design, for which The Entity-Relationship (ER) Model is a popular high-level conceptual data model. In the ER model, the main concepts are entity, attribute, and relationship. 2.8.1 Entities and Attributes Entity: An entity represents some "thing" (in the miniworld) that is of interest to us, i.e., about which we want to maintain some data. An entity could represent a physical object (e.g., house, person, automobile, widget) or a less tangible concept (e.g., company, job, academic course). Attribute: An entity is described by its attributes, which are properties characterizing it. Each attribute has a value drawn from some domain (set of meaningful values).
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Example: A PERSON entity might be described by Name, BirthDate, Sex, etc., attributes, each having a particular value. What distinguishes an entity from an attribute is that the latter is strictly for the purpose of describing the former and is not, in and of itself, of interest to us. It is sometimes said that an entity has an independent existence, whereas an attribute does not. In performing data modeling, however, it is not always clear whether a particular concept deserves to be classified as an entity or "only" as an attribute. We can classify attributes along these dimensions:
simple/atomic vs. composite
single-valued vs. multi-valued (or set-valued)
Stored vs. derived (Note from instructor: this seems like an implementational detail that ought not to be considered at this (high) level of abstraction.)
A composite attribute is one that is composed of smaller parts. An atomic attribute is indivisible or indecomposable.
Example 1: A BirthDate attribute can be viewed as being composed of (sub-)attributes for month, day, and year.
Example 2: An Address attribute (Figure 3.4, page 64) can be viewed as being composed of (sub-)attributes for street address, city, state, and zip code. A street address can itself be viewed as being composed of a number, street name, and apartment number. As this suggests, composition can extend to a depth of two (as here) or more.
To describe the structure of a composite attribute, one can draw a tree (as in the aforementioned Figure 3.4). In case we are limited to using text, it is customary to write its name followed by a parenthesized list of its sub-attributes. For the examples mentioned above, we would write BirthDate(Month,Day,Year) Address(StreetAddr(StrNum, StrName, AptNum), City, State, Zip) Single- vs. multi-valued attribute: Consider a PERSON entity. The person it represents has (one) SSN, (one) date of birth, (one, although composite) name, etc. But that person may have zero or more academic degrees, dependents, or (if the person is a male living in Utah) spouses! How can we model this via attributes AcademicDegrees, Dependents, and Spouses? One way is to allow such attributes to be multi-valued (perhaps set-valued is a better term), which is to say that we assign to them a (possibly empty) set of values rather than a single value.
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To distinguish a multi-valued attribute from a single-valued one, it is customary to enclose the former within curly braces (which makes sense, as such an attribute has a value that is a set, and curly braces are traditionally used to denote sets). Using the PERSON example from above, we would depict its structure in text as PERSON(SSN, Name, BirthDate(Month, Day, Year), { AcademicDegrees(School, Level, Year) }, { Dependents }, ...) Here we have taken the liberty to assume that each academic degree is described by a school, level (e.g., B.S., Ph.D.), and year. Thus, AcademicDegrees is not only multi-valued but also composite. We refer to an attribute that involves some combination of multi-valuedness and compositeness as a complex attribute. A more complicated example of a complex attribute is AddressPhone in Figure 3.5 (page 65). This attribute is for recording data regarding addresses and phone numbers of a business. The structure of this attribute allows for the business to have several offices, each described by an address and a set of phone numbers that ring into that office. Its structure is given by { AddressPhone( { Phone(AreaCode, Number) }, Address(StrAddr(StrNum, StrName, AptNum), City, State, Zip)) } Stored vs. derived attribute: Perhaps independent and derivable would be better terms for these (or non-redundant and redundant). In any case, a derived attribute is one whose value can be calculated from the values of other attributes, and hence need not be stored. Example: Age can be calculated from BirthDate, assuming that the current date is accessible. The Null value: In some cases a particular entity might not have an applicable value for a particular attribute. Or that value may be unknown. Or, in the case of a multi-valued attribute, the appropriate value might be the empty set. Example: The attribute DateOfDeath is not applicable to a living person and its correct value may be unknown for some persons who have died. In such cases, we use a special attribute value (non-value?), called null. There has been some argument in the database literature about whether a different approach (such as having distinct values for not applicable and unknown) would be superior. 2.8.2: Entity Types, Entity Sets, Keys, and Domains Above we mentioned the concept of a PERSON entity, i.e., a representation of a particular person via the use of attributes such as Name, Sex, etc. Chances are good that, in a database in
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which one such entity exists, we will want many others of the same kind to exist also, each of them described by the same collection of attributes. Of course, the values of those attributes will differ from one entity to another (e.g., one person will have the name "Mary" and another will have the name "Rumpelstiltskin"). Just as likely is that we will want our database to store information about other kinds of entities, such as business transactions or academic courses, which will be described by entirely different collections of attributes. This illustrates the distinction between entity types and entity instances. An entity type serves as a template for a collection of entity instances, all of which are described by the same collection of attributes. That is, an entity type is analogous to a class in object-oriented programming and an entity instance is analogous to a particular object (i.e., instance of a class). In ER modeling, we deal only with entity types, not with instances. In an ER diagram, each entity type is denoted by a rectangular box. An entity set is the collection of all entities of a particular type that exist, in a database, at some moment in time. Key Attributes of an Entity Type: A minimal collection of attributes (often only one) that, by design, distinguishes any two (simultaneously-existing) entities of that type. In other words, if attributes A1 through Am together form a key of entity type E, and e and f are two entities of type E existing at the same time, then, in at least one of the attributes Ai (0 < i M:N (yes,no)-->N:1 (no,yes)-->1:N (no, no) --> 1:1
participation: specifies whether or not the existence of an entity depends upon its being related to another entity via the relationship. o
total participation (or existence dependency): To say that entity type A is constrained to participate totally in relationship R is to say that if (at some moment in time) R's instance set is
{ (a1, b1), (a2, b2), ... (am, bm) },
then (at that same moment) A's instance set must be { a1, a2, ..., am }. In other words, there can be no member of A's instance set that does not participate in at least one instance of R. According to our informal description of COMPANY, every employee must be assigned to some department. That is, every employee instance must participate in at least one instance of WORKS_FOR, which is to say that EMPLOYEE satisfies the total participation constraint with respect to the WORKS_FOR relationship. In an ER diagram, if entity type A must participate totally in relationship type R, the two are connected by a double line. See Figure 3.2. o
partial participation: the absence of the total participation constraint! (E.g., not every employee has to participate in MANAGES; hence we say that, with respect to MANAGES, EMPLOYEE participates partially. This is not to say that for all employees to be managers is not allowed; it only says that it need not be the case that all employees are managers.
2.9.4 Attributes of Relationship Types Relationship types, like entity types, can have attributes. A good example is WORKS_ON, each instance of which identifies an employee and a project on which (s)he works. In order to record
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(as the specifications indicate) how many hours are worked by each employee on each project, we include Hours as an attribute of WORKS_ON. (See Figure 3.2 again.) In the case of an M:N relationship type (such as WORKS_ON), allowing attributes is vital. In the case of an N:1, 1:N, or 1:1 relationship type, any attributes can be assigned to the entity type opposite from the 1 side. For example, the StartDate attribute of the MANAGES relationship type can be given to either the EMPLOYEE or the DEPARTMENT entity type. 2.10 Weak Entity Types: An entity type that has no set of attributes that qualify as a key is called weak. (Ones that do are strong.) An entity of a weak identity type is uniquely identified by the specific entity to which it is related (by a so-called identifying relationship that relates the weak entity type with its so-called identifying or owner entity type) in combination with some set of its own attributes (called a partial key). Example: A DEPENDENT entity is identified by its first name together with the EMPLOYEE entity to which it is related via DEPENDS_ON. (Note that this wouldn't work for former heavyweight boxing champion George Foreman's sons, as they all have the name "George"!) Because an entity of a weak entity type cannot be identified otherwise, that type has a total participation constraint (i.e., existence dependency) with respect to the identifying relationship. This should not be taken to mean that any entity type on which a total participation constraint exists is weak. For example, DEPARTMENT has a total participation constraint with respect to MANAGES, but it is not weak. In an ER diagram, a weak entity type is depicted with a double rectangle and an identifying relationship type is depicted with a double diamond. Design Choices for ER Conceptual Design: Sometimes it is not clear whether a particular miniworld concept ought to be modeled as an entity type, an attribute, or a relationship type. Here are some guidelines (given with the understanding that schema design is an iterative process in which an initial design is refined repeatedly until a satisfactory result is achieved):
As happened in our development of the ER model for COMPANY, if an attribute of entity type A serves as a reference to an entity of type B, it may be wise to refine that attribute into a binary relationship involving entity types A and B. It may well be that B has a corresponding attribute referring back to A, in which case it, too, is refined into the
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aforementioned relationship. In our COMPANY example, this was exemplified by the Projects and ControllingDept attributes of DEPARTMENT and PROJECT, respectively.
An attribute that exists in several entity types may be refined into its own entity type. For example, suppose that in a UNIVERSITY database we have entity types STUDENT, INSTRUCTOR, and COURSE, all of which have a Department attribute. Then it may be wise to introduce a new entity type, DEPARTMENT, and then to follow the preceding guideline by introducing a binary relationship between DEPARTMENT and each of the three aforementioned entity types.
An entity type that is involved in very few relationships (say, zero, one, or possibly two) could be refined into an attribute (of each entity type to which it is related).
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UNIT 3 The Relational Data Model and Relational Database Constraints The Relational Data Model and Relational Database Constraints and Relational Algebra 3.1 Relational Model Concepts 3.1.2 Characteristics of Relations 3.1.3 Relational Model Notation 3.2 Relational Model Constraints and Relational Database Schemas 3.2.1 Domain Constraints 3.2.2 Key Constraints 3.2.3 Relational Databases and Relational Database Schemas 3.2.4 Entity Integrity, Referential Integrity, and Foreign Keys 3.3 Update Operations and Dealing with Constraint Violations 3.3.1 Insert 3.3.2 Delete 3.3.3 Update: 3.3.4 Transactions and dealing with constraints 3.4 Relational Operation 3.5 Relational algebra operation Set theory Operations 3.6 JOIN Operations 3.7 Additional Relational Operations 3.8 Examples of Queries in Relational Algebra 3.9 Relational Database Design Using ER-to-Relational Mapping
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UNIT 3
The Relational Data Model and Relational Database Constraints and Relational Algebra Origins 3.1 Relational Model Concepts
Domain: A (usually named) set/universe of atomic values, where by "atomic" we mean simply that, from the point of view of the database, each value in the domain is indivisible (i.e., cannot be broken down into component parts). Examples of domains (some taken from page 147): o
USA_phone_number: string of digits of length ten
o
SSN: string of digits of length nine
o
Name: string of characters beginning with an upper case letter
o
GPA: a real number between 0.0 and 4.0
o
Sex: a member of the set { female, male }
o
Dept_Code: a member of the set { CMPS, MATH, ENGL, PHYS, PSYC, ... }
These are all logical descriptions of domains. For implementation purposes, it is necessary to provide descriptions of domains in terms of concrete data types (or formats) that are provided by the DBMS (such as String, int, boolean), in a manner analogous to how programming languages have intrinsic data types.
Attribute: the name of the role played by some value (coming from some domain) in the context of a relational schema. The domain of attribute A is denoted dom(A).
Tuple: A tuple is a mapping from attributes to values drawn from the respective domains of those attributes. A tuple is intended to describe some entity (or relationship between entities) in the miniworld. As an example, a tuple for a PERSON entity might be { Name --> "Rumpelstiltskin", Sex --> Male, IQ --> 143 }
Relation: A (named) set of tuples all of the same form (i.e., having the same set of attributes). The term table is a loose synonym. (Some database purists would argue that a table is "only" a physical manifestation of a relation.)
Relational Schema: used for describing (the structure of) a relation. E.g., R(A1, A2, ..., An) says that R is a relation with attributes A1, ... An. The degree of a relation is the number of attributes it has, here n.
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Example: STUDENT(Name, SSN, Address) (See Figure 5.1, page 149, for an example of a STUDENT relation/table having several tuples/rows.) One would think that a "complete" relational schema would also specify the domain of each attribute.
Relational Database: A collection of relations, each one consistent with its specified relational schema.
3.1.2 Characteristics of Relations Ordering of Tuples: A relation is a set of tuples; hence, there is no order associated with them. That is, it makes no sense to refer to, for example, the 5th tuple in a relation. When a relation is depicted as a table, the tuples are necessarily listed in some order, of course, but you should attach no significance to that order. Similarly, when tuples are represented on a storage device, they must be organized in some fashion, and it may be advantageous, from a performance standpoint, to organize them in a way that depends upon their content. Ordering of Attributes: A tuple is best viewed as a mapping from its attributes (i.e., the names we give to the roles played by the values comprising the tuple) to the corresponding values. Hence, the order in which the attributes are listed in a table is irrelevant. (Note that, unfortunately, the set theoretic operations in relational algebra (at least how E&N define them) make implicit use of the order of the attributes. Hence, E&N view attributes as being arranged as a sequence rather than a set.) Values of Attributes: For a relation to be in First Normal Form, each of its attribute domains must consist of atomic (neither composite nor multi-valued) values. Much of the theory underlying the relational model was based upon this assumption. Chapter 10 addresses the issue of including non-atomic values in domains. (Note that in the latest edition of C.J. Date's book, he explicitly argues against this idea, admitting that he has been mistaken in the past.) The Null value: used for don't know, not applicable. Interpretation of a Relation: Each relation can be viewed as a predicate and each tuple in that relation can be viewed as an assertion for which that predicate is satisfied (i.e., has value true) for the combination of values in it. In other words, each tuple represents a fact. Example (see Figure 5.1): The first tuple listed means: There exists a student having name Benjamin Bayer, having SSN 305-61-2435, having age 19, etc.
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Keep in mind that some relations represent facts about entities (e.g., students) whereas others represent facts about relationships (between entities). (e.g., students and course sections). The closed world assumption states that the only true facts about the miniworld are those represented by whatever tuples currently populate the database. 3.1.3 Relational Model Notation:
R(A1, A2, ..., An) is a relational schema of degree n denoting that there is a relation R having as its attributes A1, A2, ..., An.
By convention, Q, R, and S denote relation names.
By convention, q, r, and s denote relation states. For example, r(R) denotes one possible state of relation R. If R is understood from context, this could be written, more simply, as r.
By convention, t, u, and v denote tuples.
The "dot notation" R.A (e.g., STUDENT.Name) is used to qualify an attribute name, usually for the purpose of distinguishing it from a same-named attribute in a different relation (e.g., DEPARTMENT.Name).
3.2 Relational Model Constraints and Relational Database Schemas Constraints on databases can be categorized as follows:
inherent model-based: Example: no two tuples in a relation can be duplicates (because a relation is a set of tuples)
schema-based: can be expressed using DDL; this kind is the focus of this section.
application-based: are specific to the "business rules" of the miniworld and typically difficult or impossible to express and enforce within the data model. Hence, it is left to application programs to enforce.
Elaborating upon schema-based constraints: 3.2.1 Domain Constraints: Each attribute value must be either null (which is really a non-value) or drawn from the domain of that attribute. Note that some DBMS's allow you to impose the not null constraint upon an attribute, which is to say that that attribute may not have the (non-)value null. 3.2.2 Key Constraints: A relation is a set of tuples, and each tuple's "identity" is given by the values of its attributes. Hence, it makes no sense for two tuples in a relation to be identical
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(because then the two tuples are actually one and the same tuple). That is, no two tuples may have the same combination of values in their attributes. Usually the miniworld dictates that there be (proper) subsets of attributes for which no two tuples may have the same combination of values. Such a set of attributes is called a superkey of its relation. From the fact that no two tuples can be identical, it follows that the set of all attributes of a relation constitutes a superkey of that relation. A key is a minimal superkey, i.e., a superkey such that, if we were to remove any of its attributes, the resulting set of attributes fails to be a superkey. Example: Suppose that we stipulate that a faculty member is uniquely identified by Name and Address and also by Name and Department, but by no single one of the three attributes mentioned. Then { Name, Address, Department } is a (non-minimal) superkey and each of { Name, Address } and { Name, Department } is a key (i.e., minimal superkey). Candidate key: any key! (Hence, it is not clear what distinguishes a key from a candidate key.) Primary key: a key chosen to act as the means by which to identify tuples in a relation. Typically, one prefers a primary key to be one having as few attributes as possible. 3.2.3 Relational Databases and Relational Database Schemas A relational database schema is a set of schemas for its relations (see Figure 5.5, page 157) together with a set of integrity constraints. A relational database state/instance/snapshot is a set of states of its relations such that no integrity constraint is violated. (See Figure 5.6, page 159, for a snapshot of COMPANY.) 3.2.4 Entity Integrity, Referential Integrity, and Foreign Keys Entity Integrity Constraint: In a tuple, none of the values of the attributes forming the relation's primary key may have the (non-)value null. Or is it that at least one such attribute must have a non-null value? In my opinion, E&N do not make it clear! Referential Integrity Constraint: (See Figure 5.7) A foreign key of relation R is a set of its attributes intended to be used (by each tuple in R) for identifying/referring to a tuple in some relation S. (R is called the referencing relation and S the referenced relation.) For this to make sense, the set of attributes of R forming the foreign key should "correspond to" some superkey of S. Indeed, by definition we require this superkey to be the primary key of S. This constraint says that, for every tuple in R, the tuple in S to which it refers must actually be in S. Note that a foreign key may refer to a tuple in the same relation and that a foreign key may be
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part of a primary key (indeed, for weak entity types, this will always occur). A foreign key may have value null (necessarily in all its attributes??), in which case it does not refer to any tuple in the referenced relation. Semantic Integrity Constraints: application-specific restrictions that are unlikely to be expressible in DDL. Examples:
salary of a supervisee cannot be greater than that of her/his supervisor
salary of an employee cannot be lowered
3.3 Update Operations and Dealing with Constraint Violations For each of the update operations (Insert, Delete, and Update), we consider what kinds of constraint violations may result from applying it and how we might choose to react. 3.3.1 Insert:
domain constraint violation: some attribute value is not of correct domain
entity integrity violation: key of new tuple is null
key constraint violation: key of new tuple is same as existing one
referential integrity violation: foreign key of new tuple refers to non-existent tuple
Ways of dealing with it: reject the attempt to insert! Or give user opportunity to try again with different attribute values. 3.3.2 Delete:
Referential integrity violation: a tuple referring to the deleted one exists.
Three options for dealing with it:
Reject the deletion
Attempt to cascade (or propagate) by deleting any referencing tuples (plus those that reference them, etc., etc.)
modify the foreign key attribute values in referencing tuples to null or to some valid value referencing a different tuple
3.3.3 Update:
Key constraint violation: primary key is changed so as to become same as another tuple's
referential integrity violation: o
foreign key is changed and new one refers to nonexistent tuple
o
primary key is changed and now other tuples that had referred to this one violate the constraint
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3.3.4 Transactions: This concept is relevant in the context where multiple users and/or application programs are accessing and updating the database concurrently. A transaction is a logical unit of work that may involve several accesses and/or updates to the database (such as what might be required to reserve several seats on an airplane flight). The point is that, even though several transactions might be processed concurrently, the end result must be as though the transactions were carried out sequentially. (Example of simultaneous withdrawals from same checking account.) he Relational Algebra
Operations to manipulate relations.
Used to specify retrieval requests (queries).
Query result is in the form of a relation
3.4 Relational Operations: SELECT
and PROJECT
operations.
Set operations: These include UNION U, INTERSECTION | |, DIFFERENCE -, CARTESIAN PRODUCT X. JOIN operations
.
Other relational operations: DIVISION, OUTER JOIN, AGGREGATE FUNCTIONS. 3.4.1 SELECT
and PROJECT
SELECT
):
Selects the tuples (rows) from a relation R that satisfy a certain selection condition c
Form of the operation:
The condition c is an arbitrary Boolean expression on the attributes of R
Resulting relation has the same attributes as R
Resulting relation includes each tuple in r(R) whose attribute values satisfy the condition
c
Examples: DNO=4
(EMPLOYEE)
SALARY>30000
(EMPLOYEE)
(DNO=4 AND SALARY>25000) OR DNO=5
(EMPLOYEE)
PROJECT operation (denoted by ): Dept of ISE, SJBIT
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Keeps only certain attributes (columns) from a relation R specified in an attribute list L
Form of operation:
Resulting relation has only those attributes of R specified in L
The PROJECT operation eliminates duplicate tuples in the resulting relation so that it
(R)
L
remains a mathematical set (no duplicate elements). Example:
SEX,SALARY(EMPLOYEE)
If several male employees have salary 30000, only a single tuple is kept in the resulting relation.
Duplicate tuples are eliminated by the
operation.
Sequences of operations: Several operations can be combined to form a relational algebra expression (query) Example: Retrieve the names and salaries of employees who work in department 4: FNAME,LNAME,SALARY (
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DNO=4(EMPLOYEE) )
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Alternatively, we specify explicit intermediate relations for each step: DEPT4_EMPS
DNO=4
(EMPLOYEE)
FNAME,LNAME,SALARY(DEPT4_EMPS) Attributes can optionally be renamed
in the resulting left-hand-side relation (this may be
required for some operations that will be presented later): DEPT4_EMPS
DNO=4
(EMPLOYEE)
(FIRSTNAME,LASTNAME,SALARY)
FNAME,LNAME,SALARY
(DEPT4_EMPS)
3.5 Relational algebra operation Set theory Operations
Binary operations from mathematical set theory:
UNION: R1 R2, INTERSECTION: R1 R2, SET DIFFERENCE: R1 - R2,
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CARTESIAN PRODUCT: R1 X R2.
For , , -, the operand relations R1(A1, A2, ..., An) and R2(B1, B2, ..., Bn) must have the same number of attributes, and the domains of corresponding attributes must be compatible; that is, dom(Ai) = dom(Bi) for i=1, 2, ..., n. This condition is called union compatibility. The resulting relation for
,
, or - has the same attribute names as the first operand relation R1 (by
convention).
CARTESIAN PRODUCT R(A1, A2, ..., Am, B1, B2, ..., Bn)
R1(A1, A2, ..., Am) X R2 (B1, B2, ..., Bn)
A tuple t exists in R for each combination of tuples t1 from R1 and t2 from R2 such that: t[A1, A2, ..., Am] = t1 and t[B1, B2, ..., Bn] = t2 If R1 has n1 tuples and R2 has n2 tuples, then R will have n1*n2 tuples. CARTESIAN PRODUCT is a meaningless operation on its own. It can combine related tuples from two relations if followed by the appropriate SELECT operation. Dept of ISE, SJBIT
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Example: Combine each DEPARTMENT tuple with the EMPLOYEE tuple of the manager. DEP_EMP
DEPARTMENT X EMPLOYEE
DEPT_MANAGER
MGRSSN=SSN
(DEP_EMP)
3.6 JOIN Operations
THETA JOIN: Similar to a CARTESIAN PRODUCT followed by a SELECT. The condition c is called a join condition.
R(A1, A2, ..., Am, B1, B2, ..., Bn)
R1(A1, A2, ..., Am)
c
R2 (B1, B2, ..., Bn)
EQUIJOIN: The join condition c includes one or more equality comparisons
involving
attributes from R1 and R2. That is, c is of the form:
(Ai=Bj) AND ... AND (Ah=Bk); 1 100 THEN RETURN “HUGE” ELSEIF TOT_EMPS > 50 THEN RETURN “LARGE” ELSEIF TOT_EMPS > 30 THEN RETURN “MEDIUM” ELSE RETURN “SMALL” ENDIF;
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UNIT 6 Data Base design-1
Data Base design-1 6.1 Informal design guidelines for relation schemas 6.1.1 Semantics of relations attributes 6.2. Inference Rules 6.3 Normalization 6.3.1 First Normal Form (1NF) 6.3.2 Second Normal Form (2NF) 6.3.3 Third Normal Form (3NF 6.4 Boyce-Codd Normal Form (BCNF)
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UNIT-6 Data Base design-1 6.1 Informal design guidelines for relation schemas The four informal measures of quality for relation schema
Semantics of the attributes
Reducing the redundant values in tuples
Reducing the null values in tuples
Disallowing the possibility of generating spurious tuples
6.1.1 Semantics of relations attributes Specifies how to interpret the attributes values stored in a tuple of the relation. In other words, how the attribute value in a tuple relate to one another.
Guideline 1: Design a relation schema so that it is easy to explain its meaning. Do not combine attributes from multiple entity types and relationship types into a single relation. Reducing redundant values in tuples. Save storage space and avoid update anomalies.
Insertion anomalies.
Deletion anomalies.
Modification anomalies.
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Insertion Anomalies To insert a new employee tuple into EMP_DEPT, we must include either the attribute values for that department that the employee works for, or nulls. It's difficult to insert a new department that has no employee as yet in the EMP_DEPT relation. The only way to do this is to place null values in the attributes for employee. This causes a problem because SSN is the primary key of EMP_DEPT, and each tuple is supposed to represent an employee entity - not a department entity. Deletion Anomalies If we delete from EMP_DEPT an employee tuple that happens to represent the last employee working for a particular department, the information concerning that department is lost from the database. Modification Anomalies In EMP_DEPT, if we change the value of one of the attributes of a particular department- say the manager of department 5- we must update the tuples of all employees who work in that department. Guideline 2: Design the base relation schemas so that no insertion, deletion, or modification anomalies occur. Reducing the null values in tuples. e.g., if 10% of employees have offices, it is
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better to have a separate relation, EMP_OFFICE, rather than an attribute OFFICE_NUMBER in EMPLOYEE. Guideline 3: Avoid placing attributes in a base relation whose values are mostly null. Disallowing spurious tuples. Spurious tuples - tuples that are not in the original relation but generated by natural join of decomposed subrelations. Example: decompose EMP_PROJ into EMP_LOCS and EMP_PROJ1.
Fig. 14.5a Guideline 4: Design relation schemas so that they can be naturally JOINed on primary keys or foreign keys in a way that guarantees no spurious tuples are generated. 6.2 A functional dependency (FD) is a constraint between two sets of attributes from the database. It is denoted by X
Y
We say that "Y is functionally dependent on X". Also, X is called the left-hand side of the FD. Y is called the right-hand side of the FD. A functional dependency is a property of the semantics or meaning of the attributes, i.e., a property of the relation schema. They must hold on all relation states (extensions) of R. Relation Dept of ISE, SJBIT
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extensions r(R). A FD X
Y is a full functional dependency if removal of any attribute from X
means that the dependency does not hold any more; otherwise, it is a partial functional dependency. Examples: 1. SSN
ENAME
2. PNUMBER
{PNAME, PLOCATION}
3. {SSN, PNUMBER}
HOURS
FD is property of the relation schema R, not of a particular relation state/instance Let R be a relation schema, where X R and Y R t1, t2 r,
t1[X] = t2[X]
t1[Y] = t2[Y]
The FD X
Y holds on R if and only if for all possible relations r(R), whenever two tuples of r
agree on the attributes of X, they also agree on the attributes of Y.
the single arrow
X
the double arrow
denotes "functional dependency"
Y can also be read as "X determines Y" denotes "logical implication"
6.2.1 Inference Rules IR1. Reflexivity e.g. X
X
a formal statement of trivial dependencies; useful for derivations
IR2. Augmentation e.g. X
XZ
Y
if a dependency holds, then we can freely expand its left hand side
IR3. Transitivity e.g. X
Y
Y, Y
Z
X
Z
the "most powerful" inference rule; useful in multi-step derivations
Armstrong inference rules are sound meaning that given a set of functional dependencies F specified on a relation schema R, any dependency that we can infer from F by using IR1 through IR3 holds every relation state r of R that specifies the dependencies in F. In other words, rules can be used to derive precisely the closure or no additional FD can be derived. complete meaning that using IR1 through IR3 repeatedly to infer dependencies until no more dependencies can be inferred results in the complete set of all possible dependencies that
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can be inferred from F. In other words, given a set of FDs, all implied FDs can be derived using these 3 rules. Closure of a Set of Functional Dependencies Given a set X of FDs in relation R, the set of all FDs that are implied by X is called the closure of X, and is denoted X+. Algorithms for determining X+ X+ := X; repeat oldX+ := X+ for each FD Y
Z in F do
if Y X+ then X+ := X+ Z; until oldX+ = X+;
Example: A
BC
E
CF
B
E
CD
EF
Compute {A, B}+ of the set of attributes under this set of FDs.
Solution: Step1: Go On
round
{A, B}+ := {A, B}. the the
inner
loop
4
first
time,
once
iteration,
for
each
of
for
the
given
A
A
FDs. BC B}+
{A,
{A, B}+ := {A, B, C}. Step2: On the second iteration, for E Step3
:On
the
third
CF, {A, B, C} iteration, B
for
B {A,
E B,C}+
{A, B}+ := {A, B, C, E}. Dept of ISE, SJBIT
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Step4: On the fourth iteration, for CD
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EF remains unchanged.
Go round the inner loop 4 times again. On the first iteration result does not change; on the second it expands to {A,B,C,E,F}; On the third and forth it does not change. Now go round the inner loop 4 times. Closure does not change and so the whole process terminates,
with
+
{A,B} = {A,B,C,E,F} Example. F = { SSN
ENAME, PNUMBER
{PNAME, PLOCATION}, {SSN,PNUMBER}
HOURS } {SSN}+ = {SSN, ENAME} {PNUMBER}+ = ? {SSN,PNUMBER}+ = ? 6.3 Normalization The purpose of normalization.
The problems associated with redundant data.
The identification of various types of update anomalies such as insertion, deletion, and modification anomalies.
How to recognize the appropriateness or quality of the design of relations.
The concept of functional dependency, the main tool for measuring the appropriateness of attribute groupings in relations.
How functional dependencies can be used to group attributes into relations that are in a known normal form.
How to define normal forms for relations.
How to undertake the process of normalization.
How to identify the most commonly used normal forms, namely first (1NF), second (2NF), and third (3NF) normal forms, and Boyce-Codd normal form (BCNF).
How to identify fourth (4NF), and fifth (5NF) normal forms.
Main objective in developing a logical data model for relational database systems is to create an accurate representation of the data, its relationships, and constraints. To achieve this objective, we must identify a suitable set of relations. A technique for producing a set of relations with desirable properties, given the data requirements of an enterprise
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NORMAL FORMS A relation is defined as a set of tuples. By definition, all elements of a set are distinct; hence, all tuples in a relation must also be distinct. This means that no two tuples can have the same combination of values for all their attributes. Any set of attributes of a relation schema is called a superkey. Every relation has at least one superkey—the set of all its attributes. A key is a minimal superkey, i.e., a superkey from which we cannot remove any attribute and still have the uniqueness constraint hold. In general, a relation schema may have more than one key. In this case, each of the keys is called a candidate key. It is common to designate one of the candidate keys as the primary key of the relation. A foreign key is a key in a relation R but it's not a key (just an attribute) in other relation R' of the same schema. Integrity Constraints The entity integrity constraint states that no primary key value can be null. This is because the primary key value is used to identify individual tuples in a relation; having null values for the primary key implies that we cannot identify some tuples. The referential integrity constraint is specified between two relations and is used to maintain the consistency among tuples of the two relations. Informally, the referential integrity constraint states that a tuple in one relation that refers to another relation must refer to an existing tuple in that relation. An attribute of a relation schema R is called a prime attribute of the relation R if it is a member of any key of the relation R. An attribute is called nonprime if it is not a prime attribute—that is, if it is not a member of any candidate key. The goal of normalization is to create a set of relational tables that are free of redundant data and that can be consistently and correctly modified. This means that all tables in a relational database should be in the in the third normal form (3 NF). Normalization of data can be looked on as a process during which unsatisfactory relation schemas are decomposed by breaking up their attributes into smaller relation schemas that possess desirable properties. One objective of the original normalization process is to ensure that the update anomalies such as insertion, deletion, and modification anomalies do not occur. The most commonly used normal forms
First Normal Form (1NF)
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Second Normal Form (2NF)
Third Normal Form (3NF)
Boyce-Codd Normal Form
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Other Normal Forms
Fourth Normal Form
Fifth Normal Form
Domain Key Normal Form
6.3.1 First Normal Form (1NF)
First normal form is now considered to be part of the formal definition of a relation; historically, it was defined to disallow multivalued attributes, composite attributes, and their combinations. It states that the domains of attributes must include only atomic (simple, indivisible) values and that the value of any attribute in a tuple must be a single value from the domain of that attribute.
Practical Rule: "Eliminate Repeating Groups," i.e., make a separate table for each set of related attributes, and give each table a primary key.
Formal Definition: A relation is in first normal form (1NF) if and only if all underlying simple domains contain atomic values only.
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6.3.2 Second Normal Form (2NF) Second normal form is based on the concept of fully functional dependency. A functional X
Y
is a fully functional dependency is removal of any attribute A from X means that the dependency does not hold any more. A relation schema is in 2NF if every nonprime attribute in relation is fully functionally dependent on the primary key of the relation. It also can be restated as: a relation schema is in 2NF if every nonprime attribute in relation is not partially dependent on any key of the relation. Practical Rule: "Eliminate Redundant Data," i.e., if an attribute depends on only part of a multivalued key, remove it to a separate table. Formal Definition: A relation is in second normal form (2NF) if and only if it is in 1NF and every nonkey attribute is fully dependent on the primary key.
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6.3.3 Third Normal Form (3NF) Third normal form is based on the concept of transitive dependency. A functional dependency X
Y in a relation is a transitive dependency if there is a set of attributes Z that is not a subset
of any key of the relation, and both X
Z and Z
Y hold. In other words, a relation is in 3NF
if, whenever a functional dependency X
A holds in the relation, either (a) X is a superkey of the relation, or (b) A is a prime
attribute of the relation. Practical Rule: "Eliminate Columns not Dependent on Key," i.e., if attributes do not contribute to a description of a key, remove them to a separate table. Formal Definition: A relation is in third normal form (3NF) if and only if it is in 2NF and every nonkey attribute is nontransitively dependent on the primary key.
1NF: R is in 1NF iff all domain values are atomic. Dept of ISE, SJBIT
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2NF: R is in 2 NF iff R is in 1NF and every nonkey attribute is fully dependent on the key. 3NF: R is in 3NF iff R is 2NF and every nonkey attribute is non-transitively dependent on the key. 6.4 Boyce-Codd Normal Form (BCNF) A relation schema R is in Boyce-Codd Normal Form (BCNF) if whenever a FD X -> A holds in R, then X is a superkey of R
Each normal form is strictly stronger than the previous one:
Every 2NF relation is in 1NF Every 3NF relation is in 2NF
Every BCNF relation is in 3NF
There exist relations that are in 3NF but not in BCNF
A relation is in BCNF, if and only if every determinant is a candidate key.
Additional criteria may be needed to ensure the the set of relations in a relational database are satisfactory.
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If X
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Y is non-trivial then X is a super key
STREET CITY ZIP {CITY,STREET }
ZIP
ZIP
CITY
Insertion anomaly: the city of a zip code can’t be stored, if the street is not given
Normalization STREET ZIP
ZIP CITY
Relationship Between Normal Forms
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UNIT 7 Data base design 2
7.1 Properties of relational decomposition
7.2 Algorithms for Relational Database Schema Design 7.2.1 Decomposition and Dependency Preservation
7.2.2 Lossless-join Dependency 7.3 Multivolume Dependencies and Fourth Normal Form (4NF)
7.3.1 Fourth Normal Form (4NF) 7.4 Join Dependencies and 5 NF 7.5 Other dependencies: 7.5.1 Template Dependencies 7.5.2 Domain Key Normal Form
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UNIT-7
Data base design 2 7.1 Properties of relational decomposition
Normalization Algorithms based on FDs to synthesize 3NF and BCNF describe two desirable properties (known as properties of decomposition).
Dependency Preservation Property
Lossless join property
Dependency Preservation Property enables us to enforce a constraint on the original relation from corresponding instances in the smaller relations.
Lossless join property enables us to find any instance of the original relation from corresponding instances in the smaller relations (Both used by the design algorithms to achieve desirable decompositions). A property of decomposition, which ensures that no spurious rows are generated when relations are reunited through a natural join operation.
7.2 Algorithms for Relational Database Schema Design
Individual relations being in higher normal do not guarantee a good deign Database schema must posses additional properties to guarantee a good design.
Relation Decomposition and Insufficiency of Normal Forms Suppose R = { A1, A2, …, An} that includes all the attributes of the database. R is a universal relation schema, which states that every attribute name is unique. Using FDs, the algorithms decomposes the universal relation schema R into a set of relation schemas D = {R1, R2, …, Rn} that will become the relational database schema; D is called a decomposition of R. Each attribute in R will appear in at least one relation schema R i in the decomposition so that no attributes are lost; we have
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This is called attribute preservation condition of a decomposition.
7.2.1 Decomposition and Dependency Preservation
We want to preserve dependencies because each dependencies in F represents a constraint on the database. We would like to check easily that updates to the database do not result in illegal relations being created. It would be nice if our design allowed us to check updates without having to compute natural joins. To know whether joins must be computed, we need to determine what functional dependencies may be tested by checking each relation individually. Let F be a set of functional dependencies on schema R. Let D = {R1, R2, …, Rn} be a decomposition of R. Given a set of dependencies F on R, the projection of F on Ri, Ri(F), where Ri is a subset of R, is the set of all functional dependencies XY such that attributes in XY are all contained in Ri. Hence the projection of F on each relation schema Ri in the decomposition D is the set of FDs in F+, such that all their LHS and RHS attributes are in Ri. Hence, the projection of F on each relation schema Ri in the decomposition D is the set of functional dependencies in F+. ((R1(F)) (R2(F)) … (Rm(F)))+ = F+ i.e., the union of the dependencies that hold on each Ri belongs to D be equivalent to closure of F (all possible FDs)
/*Decompose relation, R, with functional dependencies, into relations, R1,..., Rn, with associated functional dependencies, F1,..., Fk. The decomposition is dependency preserving iff: F+=(F1
+ k)
*/
If each functional dependency specified in F either appeared directly in one of the relation schema R in the decomposition D or could be inferred from the dependencies that appear in some R.
7.2.2 Lossless-join Dependency Dept of ISE, SJBIT
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A property of decomposition, which ensures that no spurious rows are generated when relations are reunited through a natural join operation. Lossless-join property refers to when we decompose a relation into two relations - we can rejoin the resulting relations to produce the original relation.
Decompose relation, R, with functional dependencies, F, into relations, R1 and R2, with attributes, A1 and A2, and associated functional dependencies, F1 and F2.
Decompositions are projections of relational schemas R A B C
A,B
A B
B,C
B C
a1 b1 c1
a1 b1
b1 c1
a2 b2 c2
a2 b2
b2 c2
a3 b1 c3
a3 b1
b1 c3
Old tables should be derivable from the newer ones through the natural join operation A,B(R)
B,C(R)
A B C a1 b1 c1 a2 b2 c2 a3 b1 c3 a1 b1 c3 a3 b1 c1
Wrong!
R1, R2 is a lossless join decomposition of R iff the attributes common to R1 and R2 contain a key for at least one of the involved relations R A B C a1 b1 c1
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A,B
A B a1 b1
B,C
B C b1 c1
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a2 b2 c2
a2 b2
a3 b1 c1
a3 b1
A,B(R)
B,C(R)
b2 c2
=B
The decomposition is lossless iff:
A1
A1
2 2
1\A2
is in F+, or
2 \A1 is
in F+
However, sometimes there is the requirement to decompose a relation into more than two relations. Although rare, these cases are managed by join dependency and 5NF.
7.3 Multivalued Dependencies and Fourth Normal Form (4NF)
4NF associated with a dependency called multi-valued dependency (MVD). MVDs in a relation are due to first normal form (1NF), which disallows an attribute in a row from having a set of values. MVD represents a dependency between attributes (for example, A, B, and C) in a relation, such that for each value of A there is a set of values for B, and a set of values for C. However, the set of values for B and C are independent of each other. MVD between attributes A, B, and C in a relation using the following notation
A B
(A multidetermines B)
A C Formal Definition of Multivalued Dependency A multivalued dependency (MVD) X
Y specified on R, where X, and Y are both
subsets of R and Z = (R – (X Y)) specifies the following restrictions on r(R) t3[X]=t4[X]=t1[X]=t2[X] t3[Y] = t1[Y] and t4[Y] = t2[Y] t3[Z] = t2[Z] and t4[Z] = t1 [Z]
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7.3.1 Fourth Normal Form (4NF)
A relation that is in Boyce-Codd Normal Form and contains no MVDs. BCNF to 4NF involves the removal of the MVD from the relation by placing the attribute(s) in a new relation along with a copy of the determinant(s).
A Relation is in 4NF if it is in 3NF and there is no multivalued dependencies.
7.4 Join Dependencies and 5 NF A join dependency (JD), denoted by JD{R1, R2, …, Rn}, specified on relation schema R, specifies a constraint on the states r of R. The constraint states that every legal state r of R should have a lossless join decomposition into R1, R2, …, Rn; that is, for every such r we have * (R1(r), (R2(r) Dept of ISE, SJBIT
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Lossless-join property refers to when we decompose a relation into two relations - we can rejoin the resulting relations to produce the original relation. However, sometimes there is the requirement to decompose a relation into more than two relations. Although rare, these cases are managed by join dependency and 5NF.
5NF (or project-join normal form (PJNF))
A relation that has no join dependency. 7.5 Other dependencies: 7.5.1 Template Dependencies The idea behind template dependencies is to specify a template—or example—that defines each constraint or dependency. There are two types of templates: tuple-generating templates and constraint-generating templates. A template consists of a number of hypothesis tuples that are meant to show an example of the tuples that may appear in one or more relations. The other part of the template is the template conclusion. For tuple-generating templates, the conclusion is a set of tuples that must also exist in the relations if the hypothesis tuples are there. For constraintgenerating templates, the template conclusion is a condition that must hold on the hypothesis tuples.
7.5.2 Domain Key Normal Form
The idea behind domain-key normal form (DKNF) is to specify (theoretically, at least) the "ultimate normal form" that takes into account all possible types of dependencies and constraints.
A relation is said to be in DKNF if all constraints and dependencies that should hold on the relation can be enforced simply by enforcing the domain constraints and key constraints on the relation.
However, because of the difficulty of including complex constraints in a DKNF relation, its practical utility is limited, since it may be quite difficult to specify general integrity constraints.
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For example, consider a relation CAR(MAKE, VIN#) (where VIN# is the vehicle identification number) and another relation MANUFACTURE(VIN#, COUNTRY) (where COUNTRY is the country of manufacture). A general constraint may be of the following form: "If the MAKE is either Toyota or Lexus, then the first character of the VIN# is a "J" if the country of manufacture is Japan; if the MAKE is Honda or Acura, the second character of the VIN# is a "J" if the country of manufacture is Japan." There is no simplified way to represent such constraints short of writing a procedure (or general assertions) to test them.
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UNIT 8 Data base design 2
Transaction Processing Concepts 8.1 Introduction to Transaction Processing 8.2 Transactions, Read and Write Operations 8.3 Why Concurrency Control Is Needed 8.4 Why Recovery Is Needed 8.5 Transaction and System Concepts 8.6 The System Log 8.7 Desirable Properties of Transactions 8.8 Schedules and Recoverability 8.10 Characterizing Schedules Based on Recoverability
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UNIT 8 Transaction Processing Concepts 8.1 Introduction to Transaction Processing Single-User Versus Multiuser Systems
A DBMS is single-user id at most one user at a time can use the system, and it is multiuser if many users can use the system—and hence access the database— concurrently.
Most DBMS are multiuser (e.g., airline reservation system).
Multiprogramming operating systems allow the computer to execute multiple programs (or processes) at the same time (having one CPU, concurrent execution of processes is actually interleaved).
If the computer has multiple hardware processors (CPUs), parallel processing of multiple processes is possible.
8.2 Transactions, Read and Write Operations
A transaction is a logical unit of database processing that includes one or more database access operations (e.g., insertion, deletion, modification, or retrieval operations). The database operations that form a transaction can either be embedded within an application program or they can be specified interactively via a high-level query language such as SQL. One way of specifying the transaction boundaries is by specifying explicit begin transaction and end transaction statements in an application program; in this case, all database access operations between the two are considered as forming one transaction. A
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single application program may contain more than one transaction if it contains several transaction boundaries. If the database operations in a transaction do not update the database but only retrieve data, the transaction is called a read-only transaction.
Read-only transaction - do not changes the state of a database, only retrieves data.
The basic database access operations that a transaction can include are as follows: o
read_item(X): reads a database item X into a program variable X.
o
write_item(X): writes the value of program variable X into the database item named X.
Executing a read_item(X) command includes the following steps: 3.
Find the address of the disk block that contains item X. 4. Copy that disk block into a buffer in main memory (if that disk block is not already in some main memory buffer). 5. Copy item X from the buffer to the program variable named X. Executing a write_item(X) command includes the following steps:
6.
Find the address of the disk block that contains item X. 7. Copy that disk block into a buffer in main memory (if that disk block is not already in some main memory buffer). 8. Copy item X from the program variable named X into its correct location in the buffer. 9. Store the updated block from the buffer back to disk (either immediately or at some later point in time).
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8.3 Why Concurrency Control Is Needed
The Lost Update Problem. This problem occurs when two transactions that access the same database items have their operations interleaved in a way that makes the value of some database item incorrect. Suppose that transactions T1 and T2 are submitted at approximately the same time, and suppose that their operations are interleaved then the final value of item X is incorrect, because T2 reads the value of X before T1 changes it in the database, and hence the updated value resulting from T1 is lost. For example, if X = 80 at the start (originally there were 80 reservations on the flight), N = 5 (T1 transfers 5 seat reservations from the flight corresponding to X to the flight corresponding to Y), and M = 4 (T2 reserves 4 seats on X), the final result should be X = 79; but in the interleaving of operations, it is X = 84 because the update in T1 that removed the five seats from X was lost.
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The Temporary Update (or Dirty Read) Problem. This problem occurs when one transaction updates a database item and then the transaction fails for some reason. The updated item is accessed by another transaction before it is changed back to its original value. Figure 19.03(b) shows an example where T1 updates item X and then fails before completion, so the system must change X back to its original value. Before it can do so, however, transaction T2 reads the "temporary" value of X, which will not be recorded permanently in the database because of the failure of T1. The value of item X that is read by T2 is called dirty data, because it has been created by a transaction that has not completed and committed yet; hence, this problem is also known as the dirty read problem.
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The Incorrect Summary Problem. If one transaction is calculating an aggregate summary function on a number of records while other transactions are updating some of these records, the aggregate function may calculate some values before they are updated and others after they are updated. For example, suppose that a transaction T3 is calculating the total number of reservations on all the flights; meanwhile, transaction T1 is executing. If the interleaving of operations shown in Figure 19.03(c) occurs, the result of T3 will be off by an amount N because T3 reads the value of X after N seats have been subtracted from it but reads the value of Y before those N seats have been added to it. Another problem that may occur is called unrepeatable read, where a transaction T reads an item twice and the item is changed by another transaction T' between the two reads. Hence, T receives different values for its two reads of the same item. This may occur, for example, if during an airline reservation transaction, a customer is inquiring about seat availability on several flights. When the customer decides on a particular flight, the transaction then reads the number of seats on that flight a second time before completing the reservation.
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8.4 Why Recovery Is Needed Whenever a transaction is submitted to a DBMS for execution, the system is responsible for making sure that either (1) all the operations in the transaction are completed successfully and their effect is recorded permanently in the database, or (2) the transaction has no effect whatsoever on the database or on any other transactions. The DBMS must not permit some operations of a transaction T to be applied to the database while other operations of T are not. This may happen if a transaction fails after executing some of its operations but before executing all of them. Types of Failures Failures are generally classified as transaction, system, and media failures. There are several possible reasons for a transaction to fail in the middle of execution: 1. A computer failure (system crash): A hardware, software, or network error occurs in the computer system during transaction execution. Hardware crashes are usually media failures—for example, main memory failure. 2. A transaction or system error: Some operation in the transaction may cause it to fail, such as integer overflow or division by zero. Transaction failure may also occur because of erroneous parameter values or because of a logical programming error . In addition, the user may interrupt the transaction during its execution. 3. Local errors or exception conditions detected by the transaction: During transaction execution, certain conditions may occur that necessitate cancellation of the transaction. For example, data for the transaction may not be found. Notice that an exception condition , such as insufficient account balance in a banking database, may cause a transaction, such as a fund withdrawal, to be canceled. This exception should be programmed in the transaction itself, and hence would not be considered a failure. 4. Concurrency control enforcement: The concurrency control method (see Chapter 20) may decide to abort the transaction, to be restarted later, because it violates serializability (see Section 19.5) or because several transactions are in a state of deadlock.
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5. Disk failure: Some disk blocks may lose their data because of a read or write malfunction or because of a disk read/write head crash. This may happen during a read or a write operation of the transaction. 6. Physical problems and catastrophes: This refers to an endless list of problems that includes power or air-conditioning failure, fire, theft, sabotage, overwriting disks or tapes by mistake, and mounting of a wrong tape by the operator. Failures of types 1, 2, 3, and 4 are more common than those of types 5 or 6. Whenever a failure of type 1 through 4 occurs, the system must keep sufficient information to recover from the failure. Disk failure or other catastrophic failures of type 5 or 6 do not happen frequently; if they do occur, recovery is a major task. The concept of transaction is fundamental to many techniques for concurrency control and recovery from failures. 8.5 Transaction and System Concepts Transaction States and Additional Operations A transaction is an atomic unit of work that is either completed in its entirety or not done at all. For recovery purposes, the system needs to keep track of when the transaction starts, terminates, and commits or aborts (see below). Hence, the recovery manager keeps track of the following operations: o
BEGIN_TRANSACTION: This marks the beginning of transaction execution.
o
READ or WRITE: These specify read or write operations on the database items that are executed as part of a transaction.
o
END_TRANSACTION: This specifies that READ and WRITE transaction operations have ended and marks the end of transaction execution. However, at this point it may be necessary to check whether the changes introduced by the transaction can be permanently applied to the database (committed) or whether the transaction has to be aborted because it violates serializability (see Section 19.5) or for some other reason.
o
COMMIT_TRANSACTION: This signals a successful end of the transaction so that any changes (updates) executed by the transaction can be safely committed to the database and will not be undone.
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ROLLBACK (or ABORT): This signals that the transaction has ended unsuccessfully, so that any changes or effects that the transaction may have applied to the database must be undone.
Figure 19.04 shows a state transition diagram that describes how a transaction moves through its execution states. A transaction goes into an active state immediately after it starts execution, where it can issue READ and WRITE operations. When the transaction ends, it moves to the partially committed state. At this point, some recovery protocols need to ensure that a system failure will not result in an inability to record the changes of the transaction permanently (usually by recording changes in the system log ). Once this check is successful, the transaction is said to have reached its commit point and enters the committed state. Once a transaction is committed, it has concluded its execution successfully and all its changes must be recorded permanently in the database.
8.6 The System Log
To be able to recover from failures that affect transactions, the system maintains a log to keep track of all transactions that affect the values of database items.
Log records consists of the following information (T refers to a unique transaction_id): 1. [start_transaction, T]: Indicates that transaction T has started execution.
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2. [write_item, T,X,old_value,new_value]: Indicates that transaction T has changed the value of database item X from old_value to new_value. 3. [read_item, T,X]: Indicates that transaction T has read the value of database item X. 4. [commit,T]: Indicates that transaction T has completed successfully, and affirms that its effect can be committed (recorded permanently) to the database. 5. [abort,T]: Indicates that transaction T has been aborted. 8.7 Desirable Properties of Transactions Transactions should posses the following (ACID) properties: Transactions should possess several properties. These are often called the ACID properties, and they should be enforced by the concurrency control and recovery methods of the DBMS. The following are the ACID properties: 1. Atomicity: A transaction is an atomic unit of processing; it is either performed in its entirety or not performed at all. 2. Consistency preservation: A transaction is consistency preserving if its complete execution take(s) the database from one consistent state to another. 3. Isolation: A transaction should appear as though it is being executed in isolation from other transactions. That is, the execution of a transaction should not be interfered with by any other transactions executing concurrently. 4. Durability or permanency: The changes applied to the database by a committed transaction must persist in the database. These changes must not be lost because of any failure. The atomicity property requires that we execute a transaction to completion. It is the responsibility of the transaction recovery subsystem of a DBMS to ensure atomicity. If a transaction fails to complete for some reason, such as a system crash in the midst of transaction execution, the recovery technique must undo any effects of the transaction on the database. 8.8 Schedules and Recoverability A schedule (or history) S of n transactions T1, T2, ..., Tn is an ordering of the operations of the transactions subject to the constraint that, for each transaction Ti that participates in S, the operations of Ti in S must appear in the same order in which they occur in Ti. Note, however, that operations from other transactions Tj can be interleaved with the operations of Ti in S. For
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now, consider the order of operations in S to be a total ordering, although it is possible theoretically to deal with schedules whose operations form partial orders.
Similarly, the schedule for Figure 19.03(b), which we call Sb, can be written as follows, if we assume that transaction T1 aborted after its read_item(Y) operation:
Two operations in a schedule are said to conflict if they satisfy all three of the following conditions: 1. they belong to different transactions; 2. they access the same item X; and 3. at least one of the operations is a write_item(X). For example, in schedule
, the operations
conflict, as do the operations
), and the operations w1(X) and w2(X). However, the operations r1(X) and r2(X) do not conflict, since they are both read operations; the operations w2(X) and w1(Y) do not conflict, because they operate on distinct data items X and Y; and the operations r1(X) and w1(X) do not conflict, because they belong to the same transaction. A schedule S of n transactions T1, T2, ..., Tn, is said to be a complete schedule if the following conditions hold: 1. The operations in S are exactly those operations in T1, T2, ..., Tn, including a commit or abort operation as the last operation for each transaction in the schedule. 2. For any pair of operations from the same transaction Ti, their order of appearance in S is the same as their order of appearance in Ti. 3. For any two conflicting operations, one of the two must occur before the other in the schedule. 8.10 Characterizing Schedules Based on Recoverability
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once a transaction T is committed, it should never be necessary to roll back T. The schedules that theoretically meet this criterion are called recoverable schedules and those that do not are called nonrecoverable, and hence should not be permitted. A schedule S is recoverable if no transaction T in S commits until all transactions T' that have written an item that T reads have committed. A transaction T reads from transaction T in a schedule S if some item X is first written by
and later read by T. In addition,
should not
have been aborted before T reads item X, and there should be no transactions that write X after writes it and before T reads it (unless those transactions, if any, have aborted before T reads X). Consider the schedule
given below, which is the same as schedule
commit operations have been added to
except that two
:
is not recoverable, because T2 reads item X from T1, and then T2 commits before T1 commits. If T1 aborts after the c2 operation in
, then the value of X that T2 read is no longer
valid and T2 must be aborted after it had been committed, leading to a schedule that is not recoverable. For the schedule to be recoverable, the c2 operation in
must be postponed until
after T1 commits. If T1 aborts instead of committing, then T2 should also abort as shown in Se, because the value of X it read is no longer valid. In a recoverable schedule, no committed transaction ever needs to be rolled back. However, it is possible for a phenomenon known as cascading rollback (or cascading abort) to occur, where an uncommitted transaction has to be rolled back because it read an item from a transaction that failed. Serializability of Schedules
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If no interleaving of operations is permitted, there are only two possible arrangement for transactions T1 and T2. 1. Execute all the operations of T1 (in sequence) followed by all the operations of T2 (in sequence). 2. Execute all the operations of T2 (in sequence) followed by all the operations of T1
A schedule S is serial if, for every transaction T all the operations of T are executed consecutively in the schedule.
A schedule S of n transactions is serializable if it is equivalent to some serial schedule of the same n transactions.
8.11 Transaction Support in SQL
An SQL transaction is a logical unit of work (i.e., a single SQL statement).
The access mode can be specified as READ ONLY or READ WRITE. The default is READ WRITE, which allows update, insert, delete, and create commands to be executed.
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The diagnostic area size option specifies an integer value n, indicating the number of conditions that can be held simultaneously in the diagnostic area.
The isolation level option is specified using the statement ISOLATION LEVEL.
the default isolation level is SERIALIZABLE.
A sample SQL transaction might look like the following: EXEC SQL WHENEVER SQLERROR GOTO UNDO; EXEC SQL SET TRANSACTION READ WRITE DIAGNOSTICS SIZE 5 ISOLATION LEVEL SERIALIZABLE;
EXEC SQL INSERT INTO EMPLOYEE (FNAME, LNAME, SSN, DNO, SALARY) VALUES ('Jabbar', 'Ahmad', '998877665', 2, 44000); EXEC SQL UPDATE EMPLOYEE SET SALARY = SALARY * 1.1 WHERE DNO = 2; EXEC SQL COMMIT; GOTO THE_END; UNDO: EXEC SQL ROLLBACK; THE_END: . . . ;
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