CSVTU Notes

Bcoz of some reason I was attend Chhattisgarh Swami Vivekanand Technical University, Bhilai Data Mining and Warehousing subject classes, now exams coming soon, so I need some short notes on Data Mining and Warehousing, please provide here???

[Quick Sam]

You are looking for Chhattisgarh Swami Vivekanand Technical University, Bhilai Data Mining and Warehousing subject short notes, here I am giving:

Data Integration:
Even if you are a small Credit Union, I bet your enterprise data flows through and lives in A variety of in-house and external systems.
You want to ask questions that represent those
Slices of key information (referred to as Key Performance
Indicators or KPIs) such as -
What is the member profitability or member value attrition? Oh,
by the way, you want to
Be able to analyze it across all products by location, time and
channel. You realize that all
The required data is probably there but not integrated and
organized in a way for you to
Get the answers easily.
Perhaps your IT staff has been providing the reports you need
every time through a series
Of manual and automated steps of stripping or extracted the data
from one source, sorting/ merging with data from other sources,
manually scrubbing and enriching the data and then running
reports against it.
CSVTU Data Mining and Warehousing notes
Data Mining and Warehousing
Unit-1
Overview and Concepts
Need For data warehousing: The Need for Data Ware
housing is as follows
Data Integration: Even if you are a small Credit Union, I bet
your enterprise data flows through and lives in
A variety of in-house and external systems. You want to ask
questions that represent those
Slices of key information (referred to as Key Performance
Indicators or KPIs) such as -
What is the member profitability or member value attrition? Oh,
by the way, you want to
Be able to analyze it across all products by location, time and
channel. You realize that all
The required data is probably there but not integrated and
organized in a way for you to
Get the answers easily.
Perhaps your IT staff has been providing the reports you need
every time through a series
Of manual and automated steps of stripping or extracted the data
from one source, sorting/ merging with data from other sources,
manually scrubbing and enriching the data and then running
reports against it.

You wonder there ought to be a better and reliable way of doing
this.Data Warehouse serves not only as a repository for
historical data but also as an excellent
Data integration platform. The data in the data warehouse is
integrated, subject oriented,
Time-variant and non-volatile to enable you to get a 360° view
of your organization.
Advanced Reporting & Analysis
The data warehouse is designed specifically to support querying,
reporting and analysis
Tasks. The data model is flattened (denormalized) and
structured by subject areas to make
It easier for users to get even complex summarized information
with a relatively simple
Query and perform multi-dimensional analysis. This has two
powerful benefits – multilevel
Trend analysis and end-user empowerment.
Multi-level trend analysis provides the ability to analyze key
trends at every level across
Several different dimensions, e.g., Organization, Product,
Location, Channel and Time,
And hierarchies within them. Most reporting, data analysis, and
visualization tools take
Advantage of the underlying data model to provide powerful
capabilities such as drilldown,
Roll-up, drill-across and various ways of slicing and dicing data.
The flattened data model makes it much easier for users to
understand the data and write Queries rather than work with
potentially several hundreds of tables and write long Queries
with complex table joins and clauses.

Knowledge Discovery and Decision Support
Knowledge discovery and data mining (KDD) is the automatic
extraction of non-obvious
Hidden knowledge from large volumes of data. For example,
Classification models could
Be used to classify members into low, medium and high lifetime
value. Instead of coming
Up with a one-size-fits-all product, the membership can be
divided into different clusters
Based on member profile using Clustering models, and products
could be customized for
Each cluster. Affinity groupings could be used to identify better
product bundling Strategies.
These KDD applications use various statistical and data mining
techniques and rely on
Subject oriented, summarized, cleansed and “de-noised” data
which a well designed data
Warehouse can readily provide.
The data warehouse also enables an Executive Information
System (EIS). Executives
Typically could not be expected to sift through several different
reports trying to get a Holistic picture of the organization’s
performance and make decisions. They need the
KPIs delivered to them.
Some of these KPIs may require cross product or cross
departmental analysis, which may
Be too manually intensive, if not impossible, to perform on raw
data from operational systems. This is especially relevant to
relationship marketing and profitability analysis the data in data

warehouse is already prepared and structured to support this
kind of analysis.
Performance
Finally, the performance of transactional systems and query
response time make the case
For a data warehouse. The transactional systems are meant to do
just that – perform
Transactions efficiently – and hence, are designed to optimize
frequent database reads and
Writes. The data warehouse, on the other hand, is designed to
optimize frequent complex
Querying and analysis. Some of the ad-hoc queries and
interactive analysis, which could
Be performed in few seconds to minutes on a data warehouse
could take a heavy toll on
The transactional systems and literally drag their performance
down.
Holding historical data in transactional systems for longer period
of time could also
Interfere with their performance. Hence, the historical data
needs to find its place in the Data warehouse.
Trends in Data Ware Housing:
Industries experience with
data warehousing over the last decade has provided important
lessons on what works in today’s business intelligence (BI)
solutions. It is not only these lessons, but also the emerging
trends which are also shaping our industry directions in business
solutions. As a result, our emerging reference architectures used

in building these enterprise data warehouse solutions are
changing to meet business demands.
This evolving reference architecture used in building solutions
will be overviewed, followed by the implications of these
changes. It is these evolving reference architectures that are
putting new demands on the databases that are used in
warehousing. An important point is that although many of these
concepts are not new, databases are being pushed in new ways
which are requiring further technology invention.
With the emergence and evolution of the intranet, as well as
more businesses exploiting semi-structured data, the more
traditional business models are evolving with respect to such
things as data accessibility, delivery, and concurrency.
Technology such as XML and web services becomes more
critical as databases integrate with web portals and BI tooling.
Moreover, additional demands on more broad decision making
within enterprises are causing heavy consolidation and nontraditional
mixed workloads (heavily mixing OLTP and DSS)
beyond what has been conventional in the past. Service level
agreements, as well as normal operational characteristics are not
the same (e.g., backups). Moreover, in many case consolidation
is not an option and or desired.
In such latter cases, the business question still needs to be run.
As a result, federation augmentation is also very real in
enterprise systems. Query management in a federated
environment is still a challenging task. A combination of
consolidation and federation augmentation is being seen.
In addition to heavy consolidation and federation augmentation,
both real-time (right-time) and active data warehousing systems
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