Database Management Systems (DBMS)

Dr. Subrat K Dash

LNMIIT

Course Information

Database Management Systems (DBMS) Credit: 3

4 slots per week TUE (12-1pm), WED (11-12noon), THU (10-11am), FRI

(8-9am)

DBMS Lab Credit: 2

1 lab (3 hours duration) per week

Dr. Subrat K Dash

Course content: DBMS

Introduction

Data Models, E-R Model, Relational Data Model, Relational Algebra, SQL

Relational Database Design

Transaction Management

Data Storage and Indexes

Query Processing

Advanced Topics

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Books

Ramez Elmasri and Shamkant B Navathe, Fundamentals of Database Systems, Addison Wesley.

Raghu Ramakrishnan and Johannes Gehrke, Database Management Systems, McGraw Hill.

A Silberschatz, H F Korth and S Sudarshan, Database System Concepts, McGraw Hill.

H Garcia-Molina, J D Ullman, and Jennifer Widom, Database Systems-The Complete Book, Pearson Education.

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Grading

Regular assessment 30%

Midterm 25%

Endterm 45%

Any change to this will be communicated well in advance.

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INTRODUCTION

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Database

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Journey to Database!

Evolution of Data Systems

Manual, Sequential, Databases

Technology Explosion

Data Storage Devices

Computer Applications

DP, MIS, DSS, DW&DM

Demand for Information

Inadequacy of earlier Data Systems

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Database

A database is a collection of related data. Data: Facts that can be recorded and have implicit meaning

Properties Represents some aspects of the real world Logically coherent collection of data with some inherent

meaning Designed, built and populated with data for a specific purpose

A database is an ordered collection of related data

elements intended to meet the information needs of an organization and designed to be shared by multiple users.

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Database Management System (DBMS)

DBMS A collection of programs that enables to create and maintain a database.

DBMS is a general-purpose software system that facilitates the processes of defining, constructing, manipulating, and sharing databases among various users and applications.

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A simplified database system

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An example

University Database

Data about students, faculty members, courses, laboratories etc.

Reflects the state of affairs of the academic aspects of the university.

Purpose: To keep an accurate track of the academic activities of the university.

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File System based storage

Files of records Used for data storage Data redundancy Wastage of space

Maintaining consistency becomes difficult

Record Structure Hard coded into the programs Structure modifications hard to perform

Query of varying nature

Managing concurrent access and failure recovery are difficult

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DBMS Approach

Separation of Data and Metadata

Flexibility of changing metadata

Program-data independence

Data access language SQL

Properties

Controlling Redundancy

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userSticky NoteMetadata is data that describes other data. Meta is a prefix that in most information technology usages means "an underlying definition or description."

DBMS Properties

Control Redundancy Reduces duplication and thus wastage of space is avoided Checks inconsistencies

Authorized access Control retrieval and update

Persistent storage For program objects and data structures

Storage structure for efficient query processing Backup and recovery

Recovery from hardware and software failures

Enforcing integrity constraint

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Three aspects of studying DBMS

Modeling and design of databases

Explore issues before implementation

Programming: Queries and DB operations

SQL (Structured Query Language)

DBMS implementation

Transaction management, Storage, Indexes etc.

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Data Models

Data Model: A set of concepts to describe the structure of a database at a certain level of abstraction.

Helps in communication and application development

Data Model Operations: Operations for specifying database retrievals and updates by referring to the concepts of the data model. Operations on the data model may include basic operations and user-defined operations.

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Categories of data models

Conceptual (high-level, semantic) Provide concepts that are close to the way many users

perceive data. Useful for requirements understanding. Uses entities, attributes, relationships.

Implementation (representational, record-based) Describe the logical representation of the data

without giving details of physical representation.

Physical (low-level, internal) Provide concepts that describe details of how data is

stored in the computer, like record-format, record ordering, access paths, file structure.

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E-R (Entity-Relationship) Model

A conceptual level data model. Provides the concepts of entities, relationships and

attributes. Example: University Database

Entities: student, faculty member, course, departments etc.

Relationships: enrollment relationship between student & course, employment relationship between faculty member, department etc.

Attributes: name, rollNumber, address etc., of student entity; name, empNumber, phoneNumber etc., of faculty entity etc.

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Types of Data Model

Relational Data model Based on relation (table) a set of records

Hierarchical Data Model Records, Parent-child relationship (1:N) Tree like structure

Network Data Model Records, Sets A record can have more than one parent records.

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Schemas versus Instances

Database Schema: The description of a database in a formal language. Includes descriptions of the database structure and the constraints that should hold on the database.

Schema Diagram: A diagrammatic display of (some aspects of) a database schema.

Schema Construct: A component of the schema or an object within the schema, e.g., STUDENT, COURSE.

Database Instance: The actual data stored in a database at a particular moment in time. Also called database state (or occurrence).

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Database Schema Vs. Database State

Database State: Refers to the content of a database at a moment in time.

Initial Database State: Refers to the database when it is loaded

Valid State: A state that satisfies the structure and constraints of the database.

Distinction The database schema changes very infrequently. The

database state changes every time the database is updated.

Schema is also called intension, whereas state is called extension.

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Three-Schema Architecture (Abstraction levels in a DBMS)

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V1 V2 Vm

R1 R2 Rn

F1 F2 Fp

VL LL Mapping

LL PL Mapping

View Level (VL) Schema

Logical Level (LL) Schema

Physical Level (VL) Schema

Set of views

Set of relations

Data: Set of files/ index files

LDI

PDI

Three-Schema Architecture

View Level Schema Each view describes an aspect of the database

relevant to a particular group of users. Example: Library Database

Books Purchase Section Issue/Returns Management Section Users Management Section

Each section views/uses a portion of the entire data. Views can be set up for each section of users.

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Three-Schema Architecture

Logical Level Schema

Describes the logical structure of the entire database.

No physical level details are given.

Physical Level Schema

Describes the physical structure of data in terms of record formats, file structures, indexes etc.

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Database schema based on relational data model

External (View level) Schema

Conceptual (Logical) Schema

Physical Schema

Database Schema: The description of a database in a formal language. Includes descriptions of the database structure and the constraints that should hold on the database.

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Database schema based on relational data model

Conceptual (Logical) Schema

Describes all relations in the database

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Database schema based on relational data model

Physical Schema

Describes additional storage details (secondary storage devices)

Which file organization and auxiliary data structure (indexes)

Examples: Store all relations as unsorted files of records

Create indexes on first column of Students, Faculty, Courses

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Database schema based on relational data model

External (View) Schema

Allows customized data access for different group of users

A database can have more than one external schema, but only one conceptual and physical schema

Views are not stored explicitly

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Data Independence

Physical Data Independence (PDI) The ability to modify physical level schema without

affecting the logical or view level schema.

Performance tuning modification at physical level creating a new index etc.

Logical Data Independence (LDI) The ability to change the logical level schema without

affecting the view level schemas or application programs Adding a new attribute to some relation

Deleting an attribute

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Data Independence

Only the mappings need to be changed

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V1 V2 Vm

R1 R2 Rn

VL LL Mapping

Development Process of a Database System (1/2)

Step 1. Requirements collection Data model requirements

various pieces of data to be stored and the interrelationships.

presented using a conceptual data model such as E/R model.

Functional requirements various operations that need to be performed as part

of running the enterprise. acquiring a new book, enrolling a new user, issuing a book to

the user, recording the return of a book etc.

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Development Process of a Database System (2/2)

Step 2. Convert the data model into a representational level model typically relational data model.

choose an RDBMS system and create the database.

Step 3. Convert the functional requirements into application programs programs in a high-level language that use

embedded SQL to interact with the database and carry out the required tasks.

Dr. Subrat K Dash