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7/29/2019 DBMS new.ppt
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7.1
Management of Data
Dr. Nityesh Bhatt
nityesh@imnu.ac.in
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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
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