Architecture And Process
Two common ones are two tier and three tier architectures.
Data from internal sources and external sources are extracted,
warehouse.
In two tier architecture, there is no multidimensional database or server.
Components of data warehousing
Large physical
database
This is an actual physical database into which all the data for
the data warehouse are gathered, along with the metadata
and the processing logic used to scrub, organize, package &
preprocess the data for end user access.
The logical data
warehouse
This contains all the metadata, business rules, and
processing logic required to scrub, organize, package &
preprocess the data.
It contains the information required to find and access the
actual data, wherever they actually reside.
Data mart
A data mart is a subset of the enterprise wide data
warehouse.
It performs the role of a departmental, regional or functional
data warehouse.
Decision support
systems and an
executive information
system
These are not data warehouses but application that uses the
data warehouse.
Suitability and Characteristics of data warehousing
Online Analytical Data Processing : Data Access and mining querying
and analysis
The latest development in this area is client server architecture.
The term OLAP refers to DSS & EIS computing done by end users in
online system.
In OLTP voluminous data are processed as soon as they are entered.
The OLAP is performed by the end users, where as the OLTP is done by
the IS professionals.
OLAP also enable to generate queries, requesting Adhoc reports,
conducting statistical analysis and building applications.
SQL is a non procedural and very user friendly language.
Data Mining
This refers to the process of knowledge discovery in databases,
knowledge extraction, data archeology etc.
All these are conducted automatically and allow quick discovery even by
the novice users.
Main objectives
Competitive Advantages
1) Marketing: Predicting which customer will respond to a mailing
or buy a particular product. Helps to classify the customer.
2) Banking : Forecasting levels of bad loans, credit card usage
3) Retailing and Sales 8) Government & defense
4) Manufacturing & production 9) Airlines
5) Brokerage & securities trading 10) Health care
6) Insurance 11) Broadcasting
7) Computer hardware &
software
Data Visualization
Data visualization refers to technologies that support visualization of
information.
It includes digital images, Geographic information systems, GUI,
multidimensional, tables and graphs, virtual reality and animation etc.
Data visualization is easy to implement when the necessary data are in a
data warehouse.
Intelligent Database and Data mining
AI technologies, especially expert systems (ES) and artificial neural
networks (ANN) can make the access and manipulation of complex
databases simpler.
The main types of tools used in intelligent data mining
Case based
reasoning
Using historical cases, this approach can be used to
recognize patterns
Neural computing It is a machine learning approach by which historical data
can be examined for pattern recognition.
This can be used to identify potential customers of a new
product, financial services and also in manufacturing.
Intelligent agents This is one of the promising issues in retrieving information
from the databases especially external ones.
Two common ones are two tier and three tier architectures.
Data from internal sources and external sources are extracted,
warehouse.
In two tier architecture, there is no multidimensional database or server.
Components of data warehousing
Large physical
database
This is an actual physical database into which all the data for
the data warehouse are gathered, along with the metadata
and the processing logic used to scrub, organize, package &
preprocess the data for end user access.
The logical data
warehouse
This contains all the metadata, business rules, and
processing logic required to scrub, organize, package &
preprocess the data.
It contains the information required to find and access the
actual data, wherever they actually reside.
Data mart
A data mart is a subset of the enterprise wide data
warehouse.
It performs the role of a departmental, regional or functional
data warehouse.
Decision support
systems and an
executive information
system
These are not data warehouses but application that uses the
data warehouse.
Suitability and Characteristics of data warehousing
Online Analytical Data Processing : Data Access and mining querying
and analysis
The latest development in this area is client server architecture.
The term OLAP refers to DSS & EIS computing done by end users in
online system.
In OLTP voluminous data are processed as soon as they are entered.
The OLAP is performed by the end users, where as the OLTP is done by
the IS professionals.
OLAP also enable to generate queries, requesting Adhoc reports,
conducting statistical analysis and building applications.
SQL is a non procedural and very user friendly language.
Data Mining
This refers to the process of knowledge discovery in databases,
knowledge extraction, data archeology etc.
All these are conducted automatically and allow quick discovery even by
the novice users.
Main objectives
Competitive Advantages
1) Marketing: Predicting which customer will respond to a mailing
or buy a particular product. Helps to classify the customer.
2) Banking : Forecasting levels of bad loans, credit card usage
3) Retailing and Sales 8) Government & defense
4) Manufacturing & production 9) Airlines
5) Brokerage & securities trading 10) Health care
6) Insurance 11) Broadcasting
7) Computer hardware &
software
Data Visualization
Data visualization refers to technologies that support visualization of
information.
It includes digital images, Geographic information systems, GUI,
multidimensional, tables and graphs, virtual reality and animation etc.
Data visualization is easy to implement when the necessary data are in a
data warehouse.
Intelligent Database and Data mining
AI technologies, especially expert systems (ES) and artificial neural
networks (ANN) can make the access and manipulation of complex
databases simpler.
The main types of tools used in intelligent data mining
Case based
reasoning
Using historical cases, this approach can be used to
recognize patterns
Neural computing It is a machine learning approach by which historical data
can be examined for pattern recognition.
This can be used to identify potential customers of a new
product, financial services and also in manufacturing.
Intelligent agents This is one of the promising issues in retrieving information
from the databases especially external ones.
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