DBMS new.ppt

7/29/2019 DBMS new.ppt

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7.1

Management of Data

Dr. Nityesh Bhatt

[email protected]


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7.2

The Data Hierarchy

Figure 7-1


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7.3

Entities and Attributes

Figure 7-2


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7.4

Database Management System (DBMS)

Software for creating and maintaining databases

Acts as interface between application programsand data files

Separates logical and design views of data


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7.5

Traditional File Processing

Figure 7-3


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7.6

Problems with the Traditional File

Environment

Data Redundancy

Data Inconsistency

Program Data Dependence Lack of Flexibility

Poor Security

Lack of Data Sharing & Availability


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The Contemporary Database Environment

Figure 7-4


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Components of DBMS:

Data definition language:Specifies content andstructure of database and defines each data

element

Data manipulation language: Used to process

data in a database

Data control language: Used to control data in adatabase

Data dictionary: Stores definitions of data

elements and data characteristics


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Types of Databases

Hierarchical and network DBMS

Relational DBMS

Object-oriented databases


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Relational DBMS:

Represents data as two-dimensional tables calledrelations

Relates data across tables based on commondata element

Concept ofPrimary, Foreign, Candidate,Alternate, Composite Key (s)

Examples: Oracle, DB2, MS SQL Server


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The Relational Data Model

Figure 7-7


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The Three Basic Operations of a Relational DBMS

Figure 7-8


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Stores data and procedures as objects that can be

retrieved and shared automatically

Object-Oriented Databases


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Identification of Entities

Data Attributes/ Fields

Data Type

Data Size

Constraints

Establishing Relationship

Normalisation

Designing Databases:


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An Unnormalized Relation for ORDER

Figure 7-9


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Normalized Tables Created from

ORDER

Figure 7-10


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Centralised Vs. Decentralised

Database


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Distributed Databases

Figure 7-11


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Ensuring Data Quality:

Accuracy

Completeness

Relevance

Timeliness

What is Data Quality Audit, Data Cleansing?


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7.20

Online Analytical Processing (OLAP):

Multidimensional data analysis (used for BI)

Supports manipulation and analysis of large

volumes of data from multiple dimensions/

perspectives

Multidimensional Data Analysis


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7.21

MULTIDIMENSIONAL DATA MODEL


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7.22

Stores current and historical data

Supports reporting and query tools

Consolidates data for management analysis and

decision making

Extract, Transform and Load (ETL)

Data Warehousing

What is Data Mart/ Data Mining ?


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7.23

COMPONENTS OF A DATA WAREHOUSE


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7.24

Data mining:

More discovery driven than OLAP

Finds hidden patterns, relationships in large databases and infers rules to

predict future behavior

E.g., Finding patterns in customer data for one-to-one marketing

campaigns or to identify profitable customers.

Types of information obtainable from data mining

Associations

Sequences

Classification

Clustering

Forecasting

Using Databases to Improve Business Performance

and Decision Making


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7.25

Database Presence on Web

Hypermedia Database

Big Data

DATABASE TRENDS


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7.26

Unexpected Growth in Structured & UnStructured

Data

Exceeds the processing capacity of conventional

DBMS (90mn Tweets/Day, Walmart 1 Mn

trans/hour, Facebook 30 bn content)

Characteristics:

Volume: doubles every year

Velocity

Variety

Big Data Software Stack : Hadoop

Big Data


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Establishing an information policy

Firms rules, procedures, roles for sharing, managing, standardizing

data

Data administration:

Firm function responsible for specific policies and procedures to

manage data

Data governance:

Policies and processes for managing availability, usability, integrity,

and security of enterprise data, especially as it relates to governmentregulations

Database administration:

Defining, organizing, implementing, maintaining database; performed

by database design and management group

Managing Data Resources

Documents

DBMS new.ppt