The document discusses data warehouses, including their history, structure, components, applications, and how they support decision making processes by storing integrated data from various sources over time to enable analysis and informed decisions. A data warehouse contains subject-oriented data from multiple sources that is integrated, non-volatile, and time-variant to support reporting, analysis, and decision support systems. It also reviews the various components involved in collecting, transforming, storing, and accessing the data to make it available for analysis.

1. PRESENTATION
ON
DATA WAREHOUSE
Database wareh

2. INTRODUCTION OF
DATABASE
 First used by William Inmon in the early
1980s.
 A data warehouse is a subject
oriented,integrated,time variant, and volatile
collection of data in support of management
decision making process.

3.  Supports Decison Supports System(DDS).
 It is more than just data,it is also the
processes involved in getting that data from
source to table and in getting the data from
table to analysts.
 It is the data and the process
managers(load/query/warehouse) that make
information available enabling people to make
informed decisions.

4. WAREHOUSE

5. STRUCTURE OF DATA
WAREHOUSE
TIME –VARIANT
• Contain information collected over time.
• Decisions are made by analyzing past trends in
companies performance.
NON VOLATILE
• Data never updated but used only for queries.
• Change,update,delete,etc is done to only
operational data.
• i.e it is filled only with the historical data.

6. INTEGRATED
• Contains various types of data and
database integrated to make it consistent.
SUBJECT-ORIETNED
• Provides simple and concise collection of data
• Is built around all the existing applications of
operational data.

7. COMPONENTS OF DATA
WAREHOUSE
• Data sources
• Data transformations
• Reporting
• Metadata
• Operations
• Other components

9. • Data Sources
Data Sources refers to any electronic repository of
information where data is passed from these systems to
the data ware house either on a transaction-by
transaction basis for real time data warehouses or on a
regular cycle.
• Data Transformation
The Data Transformation layer receives data from the
data sources,cleans and standarizes it, and loads it in the
data repository.
•
. Data Warehouse
The data warehouse is a relational database organized to
hold information in a structure that best supports reporting
and analysis.

10. • Reporting
The data in the data ware house must be available to all the
users if the data warehouse is to be useful.
• Metadata
Metadata or “DATA ABOUT DATA” is used to inform users
of the data warehouse about its status ans the information
held within the data warehouse.
• Operations:
Data warehouse operations comprises of the
processes of loading, manipulating and extracting data
from the data warehouse. Operations also covers user
management, security, capacity management and related
functions.

11. In addition, the following components also
exist in some data warehouses:
1. Dependent Data Marts: A dependent data mart
is a physical database (either on the same hardware as
the data warehouse or on a separate hardware
platform) that receives all its information from the data
warehouse.
2. Logical Data Marts: A logical data mart is a
filtered view of the main data warehouse but does not
physically exist as a separate data copy.
3. Operational Data Store: An ODS is an
integrated database of operational data. Its sources
include legacy systems and it contains current or near
term data.

12. APPLICATIONS OF DATA
WAREHOUSE
Safety
• under the control of data ware house
users so information can be stored
safely for past.
Fast retrieval of data
•Separate from operational systems,so it
provide retrieval of data without slowind down
operation.

13. Facilitate decision support system
Facilitate DSS such as trend reports,exception
reports, and reports that show actual performance
versus goals.
Common data model
Provide common data model for all data of interest
regardless of the data’s source .
Easy to report and analyze information such as
sales invoices,order receipts,general ledger etc

14. Analytical processing
multidimensional analysis of data warehouse data
supports basic OLAP operations, slice-dice, drilling, pivoting
Information processing
supports querying, basic statistical analysis,
and reporting using crosstabs, tables, charts
and graphs
Data mining
knowledge discovery from hidden patterns
supports associations, constructing analytical models,
performing classification and prediction, and
presenting the mining results using visualization tools

15. Slide 15
Chapter
10 Decision Support Systems
Well, Sort-of

16. Slide 16
Chapter
10 Decision Support Systems
How do we define a ‘decision’?
 A position or opinion or judgment reached
after consideration
 The act of making up your mind about
something
 The commitment to irrevocably allocate
valuable resources. A decision is a
commitment to act. Action is therefore
the irrevocable allocation of valuable
resources.
 A determination of future action
 The main function of a manager

17. Slide 17
Chapter
10 Decision Support Systems
What Types of decisions are there?
 Structured Decisions: For ax + bx + c = 0, the value of x is given by:
2
Situations where the procedures to follow when a decision is
needed can be specified in advance
 Semi-structured Decisions:
Decision procedures that can be pre-
specified, but not enough to lead to a
definite recommended decision
 Unstructured Decisions:
Decision situations where it is not possible to
specify in advance most of the decision
procedures to follow

18. Slide 18
Chapter
10 Decision Support Systems
What is a decision support system?
 Computer-based information systems that supports business or
organizational decision making activities.
 DSSs serve the management, operations and planning levels of
an organization and help to make decisions, which may be rapidly
changing and not easily specified in advance.
 DSS include knowledge-based systems. A properly designed DSS
is and interactive software –based system intended to help
decision makers compiled useful information from a combination of
raw data documents or personal knowledge or business model to
identify and solve problem and make decision.

19. Slide 19
Chapter
10 Decision Support Systems
What doesn’t a decision support system do?
 Provide the solution (it is only tool)
 Be used over and over again (It was designed for unique decision
making)
 Always use the same
analytical models and tools
(The decision maker
chooses the models and
tools based on the problem
at hand)

20. Slide 20
Chapter
10 Decision Support Systems
What types of DSS analysis are there?
 What-if Analysis:
User make changes to variables, or relationships among
variables, and observe the resulting changes
 Sensitivity Analysis:
The value of only one variable is changed repeatedly and the
resulting changes in other variables are observed
 Goal-Seeking:
The value of only one variable is changed repeatedly and the
resulting changes in other variables are observed
 Optimization:
Find the optimum value for target variables given certain
constraints

21. Slide 21
Chapter
10 Decision Support Systems
What are the components of a DSS?

22. Slide 22
Chapter
10 Decision Support Systems
Database Concept
The database concept has evolved since the 1960s to ease increasing
difficulties in designing, building, and maintaining complex information
systems (typically with many concurrent end-users, and with a large amount
of diverse data).
Database is a collection of interrelated data that are organized so that it’s
contents can be easily managed accessed and updated.
A database contains a collection of related items or facts arranged in a
specific structured. The simple example of non computerized database is a
telephone directory.

23. Slide 23
Chapter
10 Decision Support Systems
Model Management System
A model management subsystem contains completed models
and the building blocks necessary to develop DSS applications.
This includes standard software with financial, statistical,
management science, or other quantitative models.
An example is Excel, with its many mathematical and statistical
functions.

24. Slide 24
Chapter
10 Decision Support Systems
The User Interface
The term user interface covers all aspects of the communications
between a user and the DSS.
Some DSS experts feel that the user interface is the most
important DSS component because much of the
power, flexibility, and ease of use of the DSS are derived from this
component.
For example, the ease of use of the interface in the Guinness DSS
enables, and encourages, managers and sales people to use the
system.

25. Slide 25
Chapter
10 Decision Support Systems
Characteristics of DSS
1. Facilitation. DSS facilitate and support specific decision-
making activities and/or decision processes.
2. Interaction. DSS are computer-based systems designed for
interactive use by decision makers or staff users who control the
sequence of interaction and the operations performed.
3. Ancillary. DSS can support decision makers at any level in an
organization. They are NOT intended to replace decision makers.

26. Slide 26
Chapter
10 Decision Support Systems
4. Repeated Use. DSS are intended for repeated use. A specific DSS may be used
routinely or used as needed for ad hoc decision support tasks.
5. Task-oriented. DSS provide specific capabilities that support one or more tasks
related to decision-making, including: intelligence and data analysis;
identification and design of alternatives; choice among alternatives; and decision
implementation.
6. Identifiable. DSS may be independent systems that collect or replicate data
from other information systems OR subsystems of a larger, more integrated
information system.
7. Decision Impact. DSS are intended to improve the accuracy, timeliness, quality
and overall effectiveness of a specific decision or a set of related decisions.

27. Slide 27
Chapter
10 Decision Support Systems
Functions of DSS
1. Information Retrieval:
Information retrieval in DSS environment refers to the act of
extracting information from a database for the purpose of making
decisions.
2. Data Reconfiguration:
Often managers using a DSS want information in a form other
that in which the data are logically represented within the
computer system.
a) Sorting
b) Joining

28. Slide 28
Chapter
10 Decision Support Systems
3. Calculator activities:
Calculator activities refer to the set of tasks that normally can be
done with a calculator.
a) Functions
b) Analysis
c) Statistical Tool
d) Optimizing Tools
e) What-if analysis(Sensitivity Analysis)

29. OLAP (online analytical processing)
OLAP (online analytical processing) is computer processing that enables a user to
easily and selectively extract and view data from different points of view.
OLAP can be used for data mining or the discovery of previously undiscerned
relationships between data items.
An OLAP database does not need to be as large as a data warehouse, since not all
transactional data is needed for trend analysis

30. Usage of OLAP

31. OLTP (online transaction process)
• OLTP (online transaction process) System deals with operational data. Operational data
are those data involved in the operation of a particular system.
• Example: In a banking System, you withdraw amount through an ATM. Then account
Number, ATM PIN Number, Amount you are withdrawing, Balance amount in account
etc. are operational data elements.
Operational Data
Operational data are local relevance
Frequent Updates
Normalized Tables
Point Query
• Examples for OLTP Queries:
• What is the Salary of Mr.John?
• What is the address and email id of the person who is the head of maths
department?

32. Compression between OLTP and OLAP

33. Data mining
• Data mining (the analysis step of the "Knowledge Discovery in Databases"
process, or KDD), is a field at the intersection of computer
science and statistics, is the process that attempts to discover patterns in
large data sets.
• It utilizes methods at the intersection of artificial intelligence, machine
learning, statistics, and database systems
• The overall goal of the data mining process is to extract information from a
data set and transform it into an understandable structure for further use
• The actual data mining task is the automatic or semi-automatic
analysis of large quantities of data to extract previously unknown
interesting patterns such as groups of data records (cluster analysis),
unusual records (anomaly detection) and dependencies

34. Application of data mining
Data understanding
Data preparation
Modeling
Evaluation
Deployment

35. Data warehouse
• A data warehouse (DW or DWH) is a database used
for reporting and data analysis.
• The data stored in the warehouse are uploaded from the operational
systems (such as marketing, sales etc., shown in the figure to the right).
The data may pass through an operational for additional operations before
they are used in the DW for reporting
• A data warehouse constructed from integrated data source systems does
not require ETL, staging databases, or operational data store databases.
The integrated data source systems may be considered to be a part of a
distributed operational data store layer

36. Application of warehouse
Develop
Design
update
Develop
staging area