Database Introduction for MIS Students.pptx

FOUNDATION CONCEPTS
DATABASE
Deepanjal Shrestha, School of Business

Fundamental Data Concepts
Data is raw bits and pieces of information
 Quantitative – numeric
 Qualitative – descriptive
 Alone is not useful
Information is when data is given context and more specific
Knowledge is developed when information has been aggregated
and analyzed to make decisions, set policies, and spark innovation
Wisdom is the combination of knowledge and
experience
 May take years to develop
Data Information Knowledge Wisdom

Basics of a Database
Databases are made up of: -files -records -fields
Files: A file is a collection of related database information.
 Ex. A Movie Collection, Address Book
Records: A record is a collection of data about a single
product, activity, or transaction.
 Ex. Titanic, Person in the Address Book
 Fields: A field is each separate piece of information (but
related) in a record. Fields are the “building blocks” for records
containing the data you want to store, find, sort and print.
 Ex. Name, Address, City, Phone #
This record has four fields.

What are Databases Good For?
Easy way to organize and sort information
Use the information (mail merge)
Analyze information
Group information
Find specific information
Look at patterns and relationships between
information
Take up less space
Help you manage large collections of information

Traditional File System Problems

Database Advantages over Traditional File System

• A database is a collection
of information that is organized so that
it can be easily accessed, managed and
updated.
• Computer databases typically contain
aggregations of data records or files,
containing information about sales
transactions or interactions with specific
customers.
Database

• Database Management System (DBMS) is a
software for storing and retrieving users' data while
considering appropriate security measures.
• It consists of a group of programs which manipulate
the database.
• The DBMS accepts the request for data from an
application and instructs the operating system to
provide the specific data.
• In large systems, a DBMS helps users and other
third-party software to store and retrieve data.
Database Management System (DBMS)

Disadvantages of DBMS
DBMS implementation cost is high compared to the
file system
Complexity: Database systems are complex to
understand
Performance: Database systems are generic,
making them suitable for various applications.
However this feature affect their performance for
some applications

• The traditional filing system (TFS) is a method of
storing and arranging computer files and the
information in the file (data).
• Basically it organizes these files into a database for
the storage, organization, manipulation, and
retrieval by the computer's operating system.

Data Model
It defines the data elements and the relationships
between the data elements.
Data Models are used to show how data is stored,
connected, accessed and updated in the database
management system.
Here, we use a set of symbols and text to represent
the information so that members of the organization
can communicate and understand it.

Types of Database Models
Data
Model
Hierarchical
Network
ER model
Relational
Multi-
dimensional
Object Oriented

Hierarchical Model
This model organizes the data in the hierarchical tree structure.
The hierarchy starts from the root which has root data and then it
expands in the form of a tree adding child node to the parent node.
This model easily represents some of the real-world relationships

Hierarchical Model
 Advantages of Hierarchical Model
 It is very simple and fast to traverse through a tree-
like structure.
 Any change in the parent node is automatically
reflected in the child node so, the integrity of data is
maintained.
 Disadvantages of Hierarchical Model
 Complex relationships are not supported.
 As it does not support more than one parent of the
child node so if we have some complex relationship
where a child node needs to have two parent node
then that can't be represented using this model.
 If a parent node is deleted then the child node is
automatically deleted.

Object Oriented Data Model
Data and their relationships are contained in a single
structure which is referred as object in this data model
 Object
 Attributes
 Methods
 Class
 Inheritance

Network Data Model

A network database model is a database model that allows
multiple records to be linked to the same owner file.
 Many-to-many relationship
 Easily accessed because of the linkage between the
information
 Great flexibility among the information files because
the multiple relationships among the files

Relational Data Model
Relational Model (RM) represents the database as a
collection of relations.
A relation is nothing but a table of values. Every row in
the table represents a collection of related data values.
These rows in the table denote a real-world entity or
relationship

ER- Data Model
Entity-relationship diagrams (ERD), or entity-relationship
models are modeled as components (entities) that are
connected to each other by relations that express the
dependencies and requirements between them.

Multi-Dimensional Data Model
A multidimensional model views data in the form
of a data-cube. A data cube enables data to be
modeled and viewed in multiple dimensions. It is
defined by dimensions and facts

DDL: Data Definition Language
A data definition language (DDL) is a computer
language used to create and modify the structure of
database objects in a database.
These database objects include views, schemas,
tables, indexes, etc.
This term is also known as data description
language in some contexts, as it describes the fields
and records in a database table.

DML: Data Manipulation Language
A data manipulation language (DML) is a family of
computer languages including commands permitting
users to manipulate data in a database.
This manipulation involves inserting data into
database tables, retrieving existing data, deleting
data from existing tables and modifying existing data.
DML is mostly incorporated in SQL databases.

DCL: Data Control Language
It is used to control access to data stored in a
database (Authorization).
In particular, it is a component of Structured Query
Language (SQL).
Data Control Language is one of the logical group in
SQL Commands.

Transaction Control Language
Transaction Control Language(TCL) commands are
used to manage transactions in the database.
These are used to manage the changes made to
the data in a table by DML statements.
It also allows statements to be grouped together
into logical transactions.

Data Mining
Data Mining is defined as a process used to extract
usable data from a larger set of any raw data.
It implies analysing data patterns in large batches
of data using one or more software.
Data mining is also known as Knowledge Discovery
in Data (KDD).
Data mining is a process used by companies to turn
raw data into useful information. By using software to
look for patterns in large batches of data, businesses
can learn more about their customers to develop
more effective marketing strategies, increase sales
and decrease costs.

Data Marts
A data mart is a simple form of data warehouse focused on
a single subject or line of business.
Using a data mart, teams can access data and gain insights
faster, because they don't have to spend time searching within
a more complex data warehouse or manually aggregating data
from different sources.

Components of DSS
Model Management System
The model management system stores models that
managers can use in their decision-making. The models
are used in decision-making regarding the financial health
of the organization and forecasting demand for a good or
service.
User Interface
The user interface includes tools that help the end-user
of a DSS to navigate through the system.
Knowledge Base
The knowledge base includes information from internal
sources (information collected in a transaction process
system) and external sources (newspapers and online
databases).

Goals of Decision Support Systems

Use of Decision Support Systems
Using a decision support system involves an interactive analytical
modeling process
 Decision makers are not demanding
pre-specified information
 They are exploring possible alternatives
What-If Analysis
 Observing how changes to selected variables affect other variables
Sensitivity Analysis
 Observing how repeated changes to a single variable affect other
variables
Goal-seeking Analysis
 Making repeated changes to selected variables until a chosen variable
reaches a target value
Optimization Analysis
 Finding an optimum value for selected variables, given certain
constraints

Types of DSS
Communication-driven DSS
Its purpose are to help conduct a meeting, or for users to collaborate.
The most common technology used to deploy the DSS is a web or
client server.
 Examples: chats and instant messaging softwares, online
collaboration and net-meeting systems.
Data-driven DSS
It is used to query a database or data warehouse to seek specific
answers for specific purposes. It is deployed via a main frame system,
client/server link, or via the web.
 Examples: computer-based databases that have a query system to
check
Document-driven DSS
The purpose of such a DSS is to search web pages and find
documents on a specific set of keywords or search terms. The usual
technology used to set up such DSSs are via the web or a
client/server system.

Types of DSS

Knowledge-driven DSS:
Knowledge-driven DSSs or 'knowledgebase' are they are known, are a
catch-all category covering a broad range of systems covering users
within the organization seting it up, but may also include others
interacting with the organization - for example, consumers of a
business. It is essentially used to provide management advice or to
choose products/services. The typical deployment technology used to
set up such systems could be slient/server systems, the web, or
software runnung on stand-alone PCs.

Model-driven DSS
Model-driven DSSs are complex systems that help analyse decisions or
choose between different options. These are used by managers and
staff members of a business, or people who interact with the
organization, for a number of purposes depending on how the model is
set up - scheduling, decision analyses etc. These DSSs can be deployed
via software/hardware in stand-alone PCs, client/server systems, or the
web.

Online Transaction Processing
An OLTP system is a common data
processing system in today's enterprises. Classic
examples of OLTP systems are order entry, retail
sales, and financial transaction systems.
These programs, which run in the background
while users continue to work on other tasks, may
require a large number of data-intensive
computations.
OLTP database systems are commonly used for
order entry, financial transactions, customer
relationship management and retail sales via the
Internet.

Microsoft Access
Microsoft Access is a database management system
(DBMS) from Microsoft that combines the
relational Microsoft Jet Database Engine with a
graphical user interface and software-development
tools.
Microsoft Access stores data in its own format based
on the Access Jet Database Engine.
It allows us to create the framework (forms, tables
and so on) for storing information in a database.
Microsoft Access allows opening the table and
scrolling through the records contained within it.
Microsoft Access has capabilities to answer more
complex requests or queries

Database Introduction for MIS Students.pptx

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