dbms notes by dinudinesh

Database Management System

DBMS Mr. D. K. Bhawnani, Lect (CSE) BIT

UNIT – I

Introduction to DBMS

Data - Data is meaningful known raw facts that can be processed and stored as information. Database - Database is a collection of interrelated and organized data. DBMS - Database Management System (DBMS) is a collection of interrelated data [usually called database] and a set of programs to access, update and manage those data [which form part of management system].

OR

It is a software package to facilitate creation and maintenance of computerized database. It is general purpose software that facilitates the following:

1. Defining: Specifying data types and structures, and constraints for data to be stored. 2. Constructing: Storing data in a storage medium. 3. Manipulating: Involves querying, updating and generating reports. 4. Sharing: Allowing multiple users and programs to access data simultaneously.

Eg. Of DBMS - Access, dBase, FileMaker Pro, and FoxBASE, ORACLE, Ingress, Informix, Sybase, etc.

Primary goals of DBMS are:

1. To provide a way to store and retrieve database information that is both convenient and efficient. 2. To manage large and small bodies of information. It involves defining structures for storage of

information and providing mechanism for manipulation of information. 3. It should ensure safety of information stored, despite system crashes or attempts at unauthorized

access. 4. If data are to be shared among several users, then system should avoid possible anomalous results.

Eg. Of DBMS applications

1. Banking – For customer information, accounts, and loans, and banking transactions. [all

transactions] 2. Airlines – For reservation and schedule information. [reservations, schedules] 3. Universities – For student information, course registrations, and grades. [registration, grades] 4. Credit Card Transactions – For purchases on credit card and generation of monthly statements. 5. Telecommunication – For keeping records of calls made, generating monthly bills, maintaining

balances on prepaid calling cards, and storing information about communication networks. 6. Finance – For storing information about holdings, sales, and purchases of financial instruments

such as stocks and bonds. 7. Sales – For customer, product, and purchase information. [customers, products, purchases] 8. Manufacturing – For management of supply chain and for tracking production of items in

factories, inventories of items in warehouses/stores, and orders for items. [production, inventory,

orders, supply chain] 9. Human Resources – For information about employees, salaries, payroll taxes and benefits, and

generation of paychecks. [employee records, salaries, tax deductions]

File systems/File processing systems

A file system is basically storing information in data structures called ‘files’ in the operating

system and manipulating this information via application programs that manipulate the files.



Disadvantages of File Processing System

1. Data Redundancy – Since different programmers create the files and application programs over a long period, the various files are likely to have different formats and the programs may be written in several programming languages. Moreover, the same information may be duplicated in several files, this duplication of data over several files is known as data redundancy. Eg. The address and telephone number of a particular customer may appear in a file that consists of saving-account records and in a file that consists of checking-account records. 2. Data Inconsistency – The various copies of same data may no longer agree i.e. various copies of the same data may contain different information. Eg. A changed customer address may be reflected in savings-account records but not elsewhere in the system. 3. Difficulty in accessing data – In a conventional file processing system it is difficult to access the data in a specific manner and it is require creating an application program to carry out each new task. Eg. Suppose that one of the bank officers needs to find out the names of all customers who live within a particular postal-code area. The officer asks the data-processing department to generate such a list. Because the designers of the original system did not anticipate this request, there is no application program on hand to meet it. 4. Data Isolation – Because data are scattered in various files, and files may be in different formats, writing new application programs to retrieve the appropriate data is difficult. 5. Integrity problems – The data stored in the database must satisfy certain types of consistency constraints. Eg. The balance of a bank account may never fall below a prescribed amount (say, $25). Developers enforce these constraints in the system by adding appropriate code in the various application programs. However, when new constraints are added, it is difficult to change the programs to enforce them. The problem is compounded when constraints involve several data items from different files. 6. Atomicity problems – A computer system, like any other mechanical or electrical device, is subject to failure. In many applications, it is crucial that, if a failure occurs, the data be restored to the consistent state that existed prior to the failure. Eg. Consider a program to transfer $50 from account A to account B. If a system failure occurs during the execution of the program, it is possible that the $50 was removed from account A but was not credited to account B, resulting in an inconsistent database state. Clearly, it is essential to database consistency that both credit and debit occur, or that neither occurs. That is, the funds transfer must be atomic – it must happen in its entirely or not at all. It is difficult to ensure atomicity in a conventional file processing system. 7. Concurrent-access anomalies – For the sake of overall performance of the system and faster response, many systems allow multiple users to update the data simultaneously. In such an environment, interaction of concurrent updates may result in inconsistent data. Eg. Consider bank account A, containing $500. If two customers withdraw funds (say $50 and $100 respectively) from account A at about the same time, the result of the concurrent executions may leave the account in an incorrect (or inconsistent) state. Suppose that the programs executing on behalf of each withdrawal read the old balance, reduce that value by the amount being withdrawn, and write the result



back. If the two programs run concurrently, they may both read the value $500, and write back $450 and $400, respectively. Depending on which one writes the value last, the account may contain either $450 or $400, rather than the correct value of $350.

To guard against this possibility, the system must maintain some form of supervision. But supervision is difficult to provide because data may be accessed by many different application programs that have not been coordinated previously. 8. Security Problems – Not every user of the database system should be able to access all the data. Eg. In a bank system, payroll personnel need to see only that part of the database that has information about the various bank employees. They do not need access to information about customer accounts. But, since application programs are added to the system in an ad hoc manner, enforcing such security constraints is difficult.

Difference between DBMS and File-processing system

DBMS File-processing Systems

1. Redundancies and inconsistencies in data are reduced due to single file formats and duplication of data is eliminated.

1. Redundancies and inconsistencies in data exist due to single file formats and duplication of data.

2. Data is easily accessed due to standard query procedures.

2. Data cannot be easily accessed due to special application programs needed to access data.

3. Isolation/retrieval of required data is possible due to common file format, and there are provisions to easily retrieve data.

3. Data isolation is difficult due to different file formats, and also because new application programs have to be written.

4. Integrity constraints, whether new or old, can be created or modified as per need.

4. Introduction of integrity constraints is tedious and again new application programs have to be written.

5. Atomicity of updates is possible. 5. Atomicity of updates may not be maintained.

6. Several users can access data at the same time i.e. concurrently without problems.

6. Concurrent accesses may cause problems such as inconsistencies.

7. Security features can be enabled in DBMS very easily.

7. It may be difficult to enforce security features.

Advantages of a DBMS over file-processing system

A DBMS has three main features: (1) Centralized data management, (2) Data independence and (3) Data integration Due to its centralized nature, the database system can overcome the disadvantages of the file-based system as discussed below.

• Minimal Data Redundancy - Since the whole data resides in one central database, the various programs in the application can access data in different data files. Hence data present in one file need not be duplicated in another. This reduces data redundancy. However, this does not mean all redundancy can be eliminated. There could be business or technical reasons for having some amount of redundancy. Any such redundancy should be carefully controlled and the DBMS should be aware of it.

• Data Consistency - Reduced data redundancy leads to better data consistency.

• Data Integration - Since related data is stored in one single database, enforcing data integrity is much easier. Moreover, the functions in the DBMS can be used to enforce the integrity rules with minimum programming in the application programs.



• Data Sharing - Related data can be shared across programs since the data is stored in a centralized manner. Even new applications can be developed to operate against the same data.

• Enforcement of Standards - Enforcing standards in the organization and structure of data files is required and also easy in a Database System, since it is one single set of programs which is always interacting with the data files.

• Application Development Ease - The application programmer need not build the functions for handling issues like concurrent access, security, data integrity, etc. The programmer only needs to implement the application business rules. This brings in application development ease. Adding additional functional modules is also easier than in file-based systems.

• Better Controls - Better controls can be achieved due to the centralized nature of the system.

• Data Independence - The architecture of the DBMS can be viewed as a 3-level system comprising the following:

- The internal or the physical level where the data resides.

- The conceptual level which is the level of the DBMS functions

- The external level which is the level of the application programs or the end user.

Data Independence is isolating an upper level from the changes in the organization or structure of a lower level. For example, if changes in the file organization of a data file do not demand for changes in the functions in the DBMS or in the application programs, data independence is achieved. Thus Data Independence can be defined as immunity of applications to change in physical representation and access technique. The provision of data independence is a major objective for database systems.

• Reduced Maintenance - Maintenance is less and easy, again, due to the centralized nature of the system.

Disadvantages of a DBMS

The following are disadvantages of DBMS 1. Setup of the database system requires more knowledge, money, skills, and time. 2. The complexity of the database may result in poor performance.

Functions of a DBMS

The functions performed by a typical DBMS are the following:

• Data Definition - The DBMS provides functions to define the structure of the data in the application. These include defining and modifying the record structure, the type and size of fields and the various constraints/conditions to be satisfied by the data in each field.

• Data Manipulation - Once the data structure is defined, data needs to be inserted, modified or deleted. The functions which perform these operations are also part of the DBMS. These function can handle planned and unplanned data manipulation needs. Planned queries are those which form part of the application. Unplanned queries are ad-hoc queries which are performed on a need basis.



• Data Security & Integrity - The DBMS contains functions which handle the security and integrity of data in the application. These can be easily invoked by the application and hence the application programmer need not code these functions in his/her programs.

• Data Recovery & Concurrency - Recovery of data after a system failure and concurrent access of records by multiple users are also handled by the DBMS.

• Data Dictionary Maintenance - Maintaining the Data Dictionary which contains the data definition of the application is also one of the functions of a DBMS.

• Performance - Optimizing the performance of the queries is one of the important functions of a DBMS. Hence the DBMS has a set of programs forming the Query Optimizer which evaluates the different implementations of a query and chooses the best among them.

Thus the DBMS provides an environment that is both convenient and efficient to use when there is a large volume of data and many transactions to be processed.

Data abstraction

It can be summed up as follows. 1. When the DBMS hides certain details of how data is stored and maintained, it provides what is

called as the abstract view of data. 2. This is to simplify user-interaction with the system. 3. Complexity (of data and data structure) is hidden from users through several levels of abstraction.

Data abstraction is used for following purposes:

1. To provide abstract view of data. 2. To hide complexity from user. 3. To simplify user interaction with DBMS.

Levels of data abstraction

There are three levels of data abstraction. 1. Physical level: It describes how a record (e.g., customer) is stored.

Features:

a) Lowest level of abstraction. b) It describes how data are actually stored. c) It describes low-level complex data structures in detail. d) At this level, efficient algorithms to access data are defined. 2. Logical level: It describes what data stored in database, and the relationships among the data.

Features:

a) It is next-higher level of abstraction. Here whole Database is divided into small simple structures. b) Users at this level need not be aware of the physical-level complexity used to implement the simple structures. c) Here the aim is ease of use. d) Generally, database administrators (DBAs) work at logical level of abstraction.



3. View level: Application programs hide details of data types. Views can also hide information (e.g., salary) for security purposes.

Features:

a) It is the highest level of abstraction. b) It describes only a part of the whole Database for particular group of users. c) This view hides all complexity. d) It exists only to simplify user interaction with system. e) The system may provide many views for the whole system.

Levels of Data Abstraction

Instances and Schemas

Instances and Schemas are similar to types and variables in programming languages.

1. Schema:

The overall design of a database is called database schema. E.g., the database consists of information about a set of customers and accounts and the relationship between them. It is analogous to variable along with its type information in a program.

Types of Schemas (partitioned according to levels of abstraction): a. Physical schema: It is database design at the physical level. It is hidden below logical

schema, and can be changed easily without affecting application programs. b. Logical schema: It is database design at the logical level. Programmers construct

applications using logical schema. It is by far the most important schema, in terms of its effect on application programs.

c. Subschema: It is schema at view level.

2. Instance:

It is the actual content of the database at a particular point in time. It is analogous to the value of a variable.

The ANSI/SPARC Architecture

A DBMS can be considered as a buffer between application programs, end users and a database designed to fulfill features of data independence. In 1975 the American National Standards Institute Standards

Planning and Requirements Committee (ANSI-SPARC) proposed three-level architecture identified three levels of abstraction. These levels are sometimes referred to as schemas or views.

1. The External or User Level: This level describes the user’s or application program’s view of the database. Several programs or users may share the same view.

View level

View 1 View 2 View n

Logical level

Physical level



2. The Conceptual Level: This level describes the organization’s view of all the data in the database, the relationships between the data and the constraints applicable to the database. This level describes a logical view of the database i.e. a view locking implementation detail.

3. The Internal or Physical Level: This level describes the way in which data is stored and the way in which data may be accessed. This level describes a physical view of the database.

Each level is described in terms of schema – a map of the database. The three-level architecture is used to implement data independence through two levels of mapping: that between the external schema and the conceptual schema and that between the conceptual schema and the physical schema.

External/ External/ External/ External/ Conceptual Conceptual Conceptual Conceptual Mapping A Mapping B Mapping C Mapping D

Conceptual/Internal Mapping Foreg., Consider a Bank System, It uses

• Customer_Details Table.

• Customer_Transactions Table. At the internal level, a Customer_Details or Customer_Transaction record can be described as a block of consecutive storage locations (for example, words of bytes). The language compiler hides this level of detail from programmers. Similarly, the database system hides the lowest-level storage details (how data is stored and accessed) from database programmers. At the conceptual level, the table definition (the attributes data type and width definition) and the interrelationship among the data is described. Finally at the external level, several views of the database are defined, and database end users are also to see those views. In addition to hiding details of the conceptual level of the database, the views also provide a security mechanism to prevent users from accessing parts of the database. For e.g., tellers in the bank will be able to see only that part of the database that has information on customer accounts; they cannot access information concerning salaries of bank employees.

Application

Programs

Application

Programs

Application

Programs

Application

Programs

Query

Processor

User/External

Schema A

User/External

Schema B User/External

Schema C

User/External

Schema D

Conceptual Schema

Internal/Physical Schema

Database



Data Independence

Data independence is the ability to modify a schema definition in one level without affecting a schema definition in a higher level is called data independence.

There are two types of ‘data independence’:

1. Physical data independence

a. It is the ability to modify the physical scheme without causing application programs to be rewritten.

b. Modifications at this level are usually to improve performance.

2. Logical data independence

a. It is the ability to modify the conceptual scheme without causing application programs to be rewritten

b. It is usually done when logical structure of database is altered. Logical data independence is harder to achieve as the application programs are usually heavily dependent on the logical structure of the data. An analogy is made to abstract data types in programming languages.

Database Users

Users are differentiated by the way they expect to interact with the system. They fall into the following categories:

1. Application programmers: They are computer professionals interacting with the system through DML calls embedded in a program written in a host language (e.g. C, PL/1, Pascal). a. These programs are called application programs. b. The DML precompiler converts DML calls (prefaced by a special character like $, #, etc.) to

normal procedure calls in a host language. c. The host language compiler then generates the object code. d. Some special types of programming languages combine Pascal-like control structures with

control structures for the manipulation of a database. e. These are sometimes called fourth-generation languages. f. They often include features to help generate forms and display data.

2. Sophisticated users: They interact with the system without writing programs. a. They form requests by writing queries in a database query language. b. These are submitted to a query processor that breaks a DML statement down into instructions

for the database manager module.

3. Specialized users: They are sophisticated users writing special database application programs. These may be CAD systems, knowledge-based and expert systems, complex data systems (audio/video), etc.

Customer_Details

Cust_Id : 101

Loan_No : 1011

Amount : 8755.00

CREATE TABLE Customer_Details(

Cust_Id Number(4),

Loan_No Number(4),

Amount Number(7,2));

Cust_Id TYPE = BYTE(4), OFFSET = 0

Loan_No TYPE = BYTE(4), OFFSET = 4

Amount TYPE = BYTE(7), OFFSET = 8

External Level

Conceptual Level

Internal Level



4. Naive users: They are unsophisticated users who interact with the system by using permanent application programs (e.g. automated teller machine).

Database Administrator

The database administrator is a person having central control over data and programs accessing that data. He coordinates all the activities of the database system; the database administrator has a good understanding of the enterprise’s information resources and needs.

Functions of a DBA

Database administrator's duties include:

1. Schema definition: the creation of the original database schema. This involves writing a set of definitions in a DDL (data storage and definition language), compiled by the DDL compiler into a set of tables stored in the data dictionary.

2. Storage structure and access method definition: writing a set of definitions translated by the data storage and definition language compiler.

3. Schema and physical organization modification: writing a set of definitions used by the DDL compiler to generate modifications to appropriate internal system tables (e.g. data dictionary). This is done rarely, but sometimes the database schema or physical organization must be modified.

4. Granting user authority to access the database: granting different types of authorization for data access to various users

5. Specifying integrity constraints: generating integrity constraints. These are consulted by the database manager module whenever updates occur.

6. Routine Maintenance: It includes the following: a. Acting as liaison with users. b. Monitoring performance and responding to changes in requirements. c. Periodically backing up the database.

Database languages

We have Data Definition Languages (DDL) to specify database schemas and Data Manipulation

Language (DML) to express database updates and queries. In practice, these are not to separate languages but are part of a single database language, like SQL.

1. Data Definition Languages (DDL)

It is the language that is used to specify database schemas by a set of definitions contained in it.

2. Data Manipulation Language (DML)

It is a language for accessing and manipulating the data organized by the appropriate data model. DML is also known as query language. There are two types of DML a) Procedural DMLs b) Declarative DMLs (non-procedural DMLs)

1. Procedural DMLs - This language requires user to specify what data is required and how to

get those data. 2. Declarative DMLs (non-procedural DMLs) - This language requires user to specify what

data is required without specifying how to get those data.



Features of DDL

1. Used to specify a database schema as a set of definitions expressed in a DDL 2. DDL statements are compiled, resulting in a set of tables stored in a special file called a data

dictionary or data directory. 3. The data directory contains metadata (data about data). 4. The storage structure and access methods used by the database system are specified by a set of

definitions in a special type of DDL called a data storage and definition language. 5. Basic idea of DDL: To hide implementation details of the database schemes from the users.

Features of DML

1. A DML is a language which enables users to access and manipulate data. 2. The goal is to provide efficient human interaction with the system. 3. There are two types of DMLs

a. Procedural: Here user specifies what data is needed and how to get it. b. Non-procedural: Here user only specifies what data is needed.

- Easier for user - May not generate code as efficient as that produced by procedural languages

DBMS System Structure and its Components

We can explain the overall structure of DBMS/System structure and its components by the diagram (on next page). 1. Database systems are partitioned into modules for different functions. Some functions (e.g. file systems) may be provided by the operating system. 2. Broadly the functional components of a database system are: a. Query Processor: It is one of the functional components of DBMS. It translates statements in a query language into low-level instructions the database manager understands. It may also attempt to find an equivalent but more efficient form.

It contains following components: a. DML compiler - It converts DDL statements to a set of tables containing metadata stored in a

data dictionary. It also performs query optimization. b. DDL interpreter – It interprets DDL statements and records definitions into data dictionary. c. Query evaluation engine – It executes low-level instructions generated by DML compiler.

They mainly deal with solving all problems related to queries and query processing. It helps database system simplify and facilitate access to data.

b. Storage Manger (Database Manager)

1. Storage manager is a program module that provides the interface between the low-level data stored in the database and the application programs and queries submitted to the system.

2. The storage manager is responsible to the following tasks: 1. interaction with the file manager 2. efficient storing, retrieving and updating of data

3. The important components include: a. File manager: It manages allocation of disk space and data structures used to represent

information on disk. b. Database manager: It is the interface between low-level data and application programs and

queries. c. Transaction manager: Transaction manager ensures that the database remains in a consistent

(correct) state despite system failures (e.g., power failures and operating system crashes) and transaction failures.

d. DML precompiler: It converts DML statements embedded in an application program to normal procedure calls in a host language. The precompiler interacts with the query processor.



e. DDL compiler: It converts DDL statements to a set of tables containing metadata stored in a data dictionary.

f. Authorization and integrity manger – It conducts integrity checks and user authority to access data.

g. Buffer manger – It is critical part of DB and stores temporary data. In addition, several data structures are required for physical system implementation:

a. Data files: They store the database itself. b. Data dictionary: It stores information about the structure of the database. It is used heavily.

Great emphasis should be placed on developing a good design and efficient implementation of the dictionary. In short, it stores metadata.

c. Indices: They provide fast access to data items holding particular values.



Data Model

A data model is collection of tools for describing

1. data 2. data relationships 3. data semantics 4. data constraints

Types of Data Models

There are basically two types of data models 1. Record based Data Models. 2. Object based Data Models.

1. Record based Data Models – In Record-based models, the database is organized in fixed-format

records of several types. A fixed number of fields, or attributes, are defined in each record type, and each field is usually of a fixed length.

The three most popular record-based data models are

1. Relational Data Model 2. Network Data Model 3. Hierarchical Data Model

1. Relational Data Model – 1. The relational model uses tables to represent the data and the relationships among those data. 2. Each table has multiple columns, and each column is identified by a unique name. 3. It is a low level model.

In this database, each row in the table represents a different customer. Relationships link rows from two tables on the basis of the key field, in this case – number.

Advantages of Relational Data Model

a. Structural Independence – Relational database model has structural independence, i.e. changes made

in the database structure do not affect the DBMS’s capability to access data. b. Simplicity – The relational model is the simplest model at the conceptual level. It allows the designer

to concentrate on the logical view of the database, leaving the physical data storage details. c. Ease of designing, implementation, maintenance, and usage – Due to the inherent features of data

independence and structural independence, and the relational model makes it easy to design, implement, maintain and use the databases.

d. Adhoc query capability – One of the main reasons for the huge popularity of the relational database model is the presence of powerful, flexible and easy-to-use query capability. The query language of the relational database model – Structure Query Language or SQL – is a fourth generation language



(4GL). A 4GL concentrates on the ‘what’ and not on the ‘how’ of the problem. Selective output can be achieved by giving a simple query. The relational database translates the user queries into the code required to extract the desired information.

Disadvantages of Relational Data Model

a. Hardware overheads – The RDBMS needs comparatively powerful hardware as it hides the implementation complexities and the physical data storage details from the users.

b. Ease of design can result in bad design – As the relational database is an easy-to-design and use system, it can result in the development and implementation of poorly designed database management systems. As the size of the database increases, several problems may creep in – system shutdown, performance degradation and data corruption.

2. Network Data Model – 1. In the network model, data are represented by collections of records. 2. Relationships among data are represented by links. 3. In this model Graph data structure is used. 4. A network model permits a record to have more than one parent

Advantages of Network Data Model a. Simplicity – The network data model is also conceptually simple and easy to design. b. Ability to handle more relationship types – The network model can handle the one-to-many and

many-to-many relationships. c. Ease of data access – In the network database terminology, a relationship is a set. Each set comprises

of two types of records – an owner record and a member record. In a network model an application can access an owner record and all the member records within a set.

d. Data Integrity – In a network model, no member can exist without an owner. A user must therefore first define the owner record and then the member record. This ensures the data integrity.

e. Data Independence – The network model draws a clear line of demarcation between the programs and the complex physical storage details. The application programs work independently of the data. Any changes made in the data characteristics do not affect the application program.

f. Database standards – The standards devised by the DBTG (Database Task Group of CODASYL Committee) form the basis of the network model. These standards were further enhanced by ANSI/SPARC (American National Standards Institute/Standards Planning and Requirements Committee) in the 1970s. All the network database management systems adhere to these standards. These standards comprise of a DDL and a DML that augments the database administration and portability.

A Sample Network Model



Disadvantages of Network Data Model

a. System complexity – In a network model, data are accessed one record at a time. This makes it

essential for the database designers, administrators, and programmers to be familiar with the internal data structures to gain access to the data. Therefore, a user-friendly database management system cannot be created using the network model.

b. Lack of structural independence – Making structural modifications to the database is very difficult in the network database model as the data access method is navigational. Any changes made to the database structure require the application programs to be modified before they can access data. Though the network database model achieves data independence, it still fails to achieve structural independence.

4. Hierarchical Model 1. In the hierarchical model, data are represented by collections of records. 2. Relationships among data are represented by links. 3. In this model Tree data structure is used. 4. There are two concepts associated with the hierarchical model – segment types and parent-child relationships. Segment type is similar to the record types in the network models. The information retrieved only by navigating from the root segment type to the nodes segment types. Thus you can access a segment

type only via its parent segment type in the parent-child relationship. The operators provided for manipulating such structures include operators for traversing hierarchic paths up and down the trees.

Advantages of hierarchical Model

1. Simplicity – Since the database is based on the hierarchical structure, the relationship between the

various layers is logically simple. Thus, the design of a hierarchical database is simple. 2. Data Security – Hierarchical model was the first database that offered the data security that is

provided and enforced by the DBMS. 3. Data Integrity – Since the hierarchical model is based on the parent/child relationship, there is always

a link between the parent segment and the child segment under it. The child segments are always automatically referenced to its parent, this model promotes data integrity.

4. Efficiency – The hierarchical database model is a very efficient one when the database contains a large number of one-to-many relationships and when the users require large number of transactions, using data whose relationships are fixed.

Disadvantages of hierarchical Model 1. Implementation Complexity – Although the hierarchical database model is conceptually simple and

easy to design, it is quite complex to implement. The database designers should have very good knowledge of the physical data storage characteristics.

2. Database management problems – If you make any changes in the database structure of a hierarchical database, then it is required to make the necessary changes in all the application programs



that access the database. Thus, maintaining the database and the applications can become very cumbersome.

3. Lack of structural independence – Structural independence exists when the changes made to the database structure does not affect the DBMS’s ability to access data. Hierarchical database systems use physical storage paths to navigate to the different data segments. So the application programmer should have a good knowledge of the relevant access paths to access the data. So if the physical structure is changed the applications will also have to be altered. Thus, in a hierarchical database the benefits of data independence are limited by structural dependence.

4. Programming complexity – Due to the structural dependence and the navigational structure, the application programmers and the end users must know precisely how the data is distributed physically in the database in order to access data. This requires knowledge of complex pointer systems, which is difficult for users who have little or no programming knowledge.

5. Implementation limitation – Many of the common relationships do not conform to the one-to-many format required by the hierarchical model. The many-to-many relationships, which are more common in real life, are very difficult to implement in a hierarchical model.

Difference between Hierarchical, Network and Relational Models

S. No. Hierarchical Data Model Network Data Model Relational Data Model

1 Relationship between records is of parent child type.

Relationship between records is expressed in the form of pointers or links.

Relationship between record is represented by a relation that contains a key for each record involved in the relations.

2 Many-to-many relationship cannot be expressed in this model.

Many-to-many relationship can also be implemented.

Many-to-many relationship can be easily implemented.

3 It is a simple, straightforward and natural method of implementing record relationships.

Record relationship implementation is quite complex due to the use of pointers.

Relationship implementation is very easy though the use of a key or composite key field(s).

4 This type of model is useful only when there is some hierarchical character in the database.

Network model is useful for representing such records which have many-to-many relationships.

Relational model is useful for representing most of the real world objects and relationships among them.

5 In order to represent links among records, pointers are used. Thus relationships among records are physical.

In Network model also the relationship among records are physical.

Relational model does not maintain physical connection among records. Data is organized logically in the form of rows and columns and stored in table.

6 Searching for a record is very difficult since one can retrieve a child only after going though its parent record.

Searching a record is easy since there are multiple access paths to a data element.

A unique, indexed key field is used to search for a data element.

7 During updation or deletion process, chance of data inconsistency is involved.

No problem of inconsistency exists in network model because a data element is physically located at just one place.

Data integrity maintaining methods like Normalization process, etc. are adopted for consistency.



2. Object Based Data Models – In Object-based models, the database is organized in real world objects of several types. A number of fields, or attributes, are defined in each object type, and each field is usually of a variable length.

The two most popular object-based data models are a. Object oriented model b. E R Model 1. Object Oriented Model -

1. The object-oriented model is based on a collection of objects, like the E-R model. 2. An object contains values stored in instance variables within the object. 3. Unlike the record-oriented models, these values are themselves objects. 4. Thus objects contain objects to an arbitrarily deep level of nesting. 5. An object also contains bodies of code that operate on the object. These bodies of code are called

methods. 6. Objects that contain the same types of values and the same methods are grouped into classes. 7. A class may be viewed as a type definition for objects. 8. Analogy: the programming language concept of an abstract data type. 9. The only way in which one object can access the data of another object is by invoking the method

of that other object. This is called sending a message to the object. 10. Internal parts of the object, the instance variables and method code, are not visible externally. 11. Result is two levels of data abstraction. For example, consider an object representing a bank account.

a. The object contains instance variables number and balance. b. The object contains a method pay-interest which adds interest to the balance. c. Under most data models, changing the interest rate entails changing code in application

programs. d. In the object-oriented model, this only entails a change within the pay-interest method.

12. Unlike entities in the E-R model, each object has its own unique identity, independent of the values it contains: a. Two objects containing the same values are distinct. b. Distinction is maintained in physical level by assigning distinct object identifiers.

Advantages of Object Oriented Data Model

a. Capability to handle large number of different data types – Traditional database models like

hierarchical, network and relational database are limited in their capability to store the different types of data. For e.g., one cannot store pictures, voices and video in these databases. But the object-oriented database can store any type of data including text, numbers, pictures, voice and video.

b. Combination of object-oriented programming and database technology – Perhaps the most significant characteristic of object-oriented database technology is that it combines object-oriented programming with database technology to provide an integrated application development system.

c. Object-oriented features improve productivity – Inheritance allows one to develop solutions to complex problems incrementally by defining new objects in terms of previously defined objects. Polymorphism and dynamic binding allow one to define operations for one object and then to share the specification of the operation with other objects. These objects can further extend this operation to provide behaviors that are unique to those objects. Dynamic binding determines at runtime, which of these operations is actually executed, depending on the class of the object requested to perform the operation. Polymorphism and dynamic binding are powerful object-oriented features that allow one to compose objects to provide solutions without having to write code that is specific to each object. All of these capabilities come together to provide significant productivity advantages to database application developers.

d. Data access – Object-oriented database represent relationships explicitly, supporting both navigational and associative access to information. As the complexity of interrelationships between information within the database increases, the greater the advantages of representing relationships explicitly.



Another benefit of using explicit relationships is the improvement in data access performance over relational value-based relationships.

Disadvantages of Object Oriented Data Model

a. Difficult to maintain – In the real world, the data model is not static. It changes as organizational

information needs change and as missing information is identified. Consequently, the definition of objects must be changed periodically and existing databases migrated to conform to the new object definitions. Object-oriented databases are semantically rich introducing a number of challenges when changing object definitions and migrating databases. Object-oriented databases have a greater challenge handling schema migration because it is not sufficient to simply migrate the data representation to conform to the changes in class specifications. One must also update the behavioral code associated with each object.

b. Not suited for all applications – Object-oriented database systems are not suited for all applications. If it is used in situations where it is not required, then it will result in performance degradation and high processing requirements. OODBMS is popular in area such as e-commerce, engineering product data management, and special purpose databases in securities and medicine. The strength of the object model is in applications where there is an underlying needed to manage complex relationships among data objects.

2. E R Model (Entity Relational Model)

1. The entity-relationship model is based on a perception of the world as consisting of a collection of basic objects (entities) and relationships among these objects.

2. It is an object-based logical model. 3. It is a high-level data model. 4. An entity is a distinguishable object that exists. 5. Each entity has associated with it a set of attributes describing it.

E.g. number and balance for an account entity. 6. A relationship is an association among several entities.

E.g. A cust_acct relationship associates a customer with each account he or she has. 7. The set of all entities or relationships of the same type is called the entity set or relationship set. 8. Another essential element is the E-R diagram in which the mapping cardinalities express the

number of entities to which another entity can be associated via a relationship set. 9. The overall logical structure of a database can be expressed graphically by an E-R diagram:

a. Rectangles: represent entity sets. b. Ellipses: represent attributes. c. Diamonds: represent relationships among entity sets. d. Lines: link attributes to entity sets and entity sets to relationships.

An example of ER model

dbms notes by dinudinesh

Documents