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Chapter 32 - Objectives

Purpose of online analytical processing (OLAP)and how OLAP differs from data warehousing.

Key features of OLAP applications.

Potential benefits associated with successfulOLAP applications.

Rules for OLAP tools and main types of toolsincluding: multi-dimensional OLAP (MOLAP),relational OLAP (ROLAP), and managed queryenvironment (MQE).


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Data Warehousing and End-User Access Tools

Accompanying growth in data warehouses isincreasing demands for more powerful accesstools providing advanced analytical

capabilities.

Key developments include:

Online analytical processing (OLAP).

SQL extensions for complex data analysis. Data mining tools.


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Introducing OLAP

The dynamic synthesis, analysis, and

consolidation of large volumes of multi-

dimensional data, Codd (1993).

Describes a technology that uses a multi-

dimensional view of aggregate data to provide

quick access to strategic information for

purposes of advanced analysis.


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Introducing OLAP

Enables users to gain a deeper understanding

and knowledge about various aspects of their

corporate data through fast, consistent,

interactive access to a wide variety of possibleviews of the data.

Allows users to view corporate data in such a

way that it is a better model of the true

dimensionality of the enterprise.


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Introducing OLAP

Can easily answer who? and what?

questions, however, ability to answer what if?

and why? type questions distinguishes OLAP

from general-purpose query tools.

Types of analysis ranges from basic navigation

and browsing (slicing and dicing) tocalculations, to more complex analyses such as

time series and complex modeling.


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OLAP Benchmarks

OLAP Council published an analytical

processing benchmark referred to as the APB-

1 (OLAP Council, 1998).

Aim is to measure a servers overall OLAP

performance rather than the performance of

individual tasks.


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OLAP Benchmarks

APB-1 assesses most common business operationsincluding:

bulk loading of data from internal/external datasources;

incremental loading of data from operational systems;

aggregation of input/level data along hierarchies;

calculation of new data based on business models;

time series analysis;

queries with a high degree of complexity;

drill-down through hierarchies;

ad hocqueries;

multiple online sessions.


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OLAP Benchmarks

OLAP applications are judged on their abilityto provide just-in-time (JIT) information, acore requirement of supporting effectivedecision-making.

Assessing a servers ability to satisfy thisrequirement is more than measuring

processing performance but includes itsabilities to model complex businessrelationships and to respond to changingbusiness requirements.


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OLAP Benchmarks

APB-1 uses a standard benchmark metric

called AQM (Analytical Queries per Minute).

AQM represents number of analytical queries

processed per minute including data loading

and computation time. Thus, AQM

incorporates data loading performance,calculation performance, and query

performance into a singe metric.


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OLAP Benchmarks

Publication of APB-1 benchmark results must

include both the database schema and all code

required for executing the benchmark.

An essential requirement of all OLAP

applications is ability to provide users with JIT

information, to make effective decisions aboutan organizations strategic directions.


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OLAP Applications

JIT information is computed data that usually

reflects complex relationships and is often

calculated on the fly.

Also, as data relationships may not be knownin advance, the data model must be flexible.


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Examples of OLAP Applications in Various

Functional Areas


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OLAP Applications

Although OLAP applications are found in

widely divergent functional areas, all have

following key features:

multi-dimensional views of data;

support for complex calculations;

time intelligence.


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OLAP Applications - Support for Complex

Calculations

Must provide a range of powerful

computational methods such as that required

by sales forecasting, which uses trend

algorithms such as moving averages andpercentage growth.

Mechanisms for implementing computationalmethods should be clear and non-procedural.


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OLAP ApplicationsTime Intelligence

Key feature of almost any analyticalapplication as performance is almost always

judged over time.

Time hierarchy is not always used in samemanner as other hierarchies.

Concepts such as year-to-date and period-over-period comparisons should be easily defined.


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Representing Multi-Dimensional Data

Example of two-dimensional query.

What is the total revenue generated by property

sales in each city, in each quarter of 1997?

Choice of representation is based on types of

queries end-user may ask.

Compare representation - three-field relationaltable versus two-dimensional matrix.


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Multi-Dimensional Data as Three-Field Table

versus Two-Dimensional Matrix


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Example of three-dimensional query.

What is the total revenue generated by property

sales for each type of property (Flat or House) in

each city, in each quarter of 1997?

Compare representation - four-field relational

table versus three-dimensional cube.


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Multi-Dimensional Data as Four-Field

Table versus Three-Dimensional Cube


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Cube represents data as cells in an array.

Relational table only represents multi-

dimensional data in two dimensions.


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Multi-Dimensional OLAP Servers

Use multi-dimensional structures to store data

and relationships between data.

Multi-dimensional structures are bestvisualized as cubes of data, and cubes within

cubes of data. Each side of cube is a dimension.

A cube can be expanded to include otherdimensions.


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A cube supports matrix arithmetic.

Multi-dimensional query response time

depends on how many cells have to be addedon the fly.

As number of dimensions increases, number of

the cubes cells increases exponentially.


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However, majority of multi-dimensionalqueries use summarized, high-level data.

Solution is to pre-aggregate (consolidate) alllogical subtotals and totals along alldimensions.

Pre-aggregation is valuable, as typical

dimensions are hierarchical in nature. (e.g. Time dimension hierarchy - years, quarters,

months, weeks, and days)


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Predefined hierarchy allows logical pre-

aggregation and, conversely, allows for a

logical drill-down.

Supports common analytical operations

Consolidation.

Drill-down.

Slicing and dicing.


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Consolidation - aggregation of data such assimple roll-ups or complex expressionsinvolving inter-related data.

Drill-Down - is reverse of consolidation andinvolves displaying the detailed data thatcomprises the consolidated data.

Slicing and Dicing - (also called pivoting) refersto the ability to look at the data from differentviewpoints.


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Multi-Dimensional OLAP servers

Can store data in a compressed form by

dynamically selecting physical storage

organizations and compression techniques that

maximize space utilization.

Dense data (i.e., data that exists for high

percentage of cells) can be stored separately

from sparse data (i.e., significant percentage of

cells are empty).


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Ability to omit empty or repetitive cells can

greatly reduce the size of the cube and the

amount of processing.

Allows analysis of exceptionally large amounts

of data.


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In summary, pre-aggregation, dimensional

hierarchy, and sparse data management can

significantly reduce the size of the cube and the

need to calculate values on-the-fly.

Removes need for multi-table joins and

provides quick and direct access to arrays of

data, thus significantly speeding up execution

of multi-dimensional queries.


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Codds Rules for OLAP Systems

In 1993, E.F. Codd formulated twelve rules as

the basis for selecting OLAP tools.

Multi-dimensional conceptual view

Transparency

Accessibility

Consistent reporting performance

Client-server architectureGeneric dimensionality


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Codds Rules for OLAP

Dynamic sparse matrix handling

Multi-user support

Unrestricted cross-dimensional operations

Intuitive data manipulation

Flexible reporting

Unlimited dimensions and aggregation levels.


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Codds Rules for OLAP Systems

There are proposals to re-define or extend the

rules. For example, to also include:

Comprehensive database management tools.

Ability to drill down to detail (source record) level. Incremental database refresh.

SQL interface to the existing enterprise

environment.


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Categories of OLAP Tools

OLAP tools are categorized according to the

architecture of the underlying database.

Three main categories of OLAP tools include Multi-dimensional OLAP (MOLAP or MD-OLAP)

Relational OLAP (ROLAP), also called multi-

relational OLAP

Managed query environment (MQE)


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Multi-Dimensional OLAP (MOLAP)

Uses specialized data structures and multi-

dimensional Database Management Systems

(MDDBMSs) to organize, navigate, and

analyze data.

Data is typically aggregated and stored

according to predicted usage to enhance query

performance.


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Use array technology and efficient storage

techniques that minimize the disk space

requirements through sparse data

management.

Provides excellent performance when data is

used as designed, and the focus is on data for a

specific decision-support application.


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Traditionally, require a tight coupling with the

application layer and presentation layer.

Recent trends segregate the OLAP from the

data structures through the use of published

application programming interfaces (APIs).


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Typical Architecture for MOLAP Tools


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MOLAP Tools - Development Issues

Underlying data structures are limited in their

ability to support multiple subject areas and to

provide access to detailed data.

Navigation and analysis of data is limited

because the data is designed according to

previously determined requirements.


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MOLAP Tools - Development Issues

MOLAP products require a different set of

skills and tools to build and maintain the

database, thus increasing the cost and

complexity of support.


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Relational OLAP (ROLAP)

Fastest growing style of OLAP technology.

Supports RDBMS products using a metadata

layer - avoids need to create a static multi-

dimensional data structure - facilitates the

creation of multiple multi-dimensional views of

the two-dimensional relation.


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Relational OLAP (ROLAP)

To improve performance, some products use

SQL engines to support complexity of multi-

dimensional analysis, while others recommend,

or require, the use of highly denormalizeddatabase designs such as the star schema.


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Typical Architecture for ROLAP Tools


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ROLAP Tools - Development Issues

Middleware to facilitate the development of

multi-dimensional applications. (Software that

converts the two-dimensional relation into a

multi-dimensional structure).

Development of an option to create persistent,

multi-dimensional structures with facilities to

assist in the administration of these structures.


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Hybrid OLAP (HOLAP)

Can use data from either a RDBMS directly or

a multi-dimension server.


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Managed Query Environment (MQE)

Relatively new development.

Provide limited analysis capability, either

directly against RDBMS products, or by usingan intermediate MOLAP server.


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Managed Query Environment (MQE)

Deliver selected data directly from DBMS or

via a MOLAP server to desktop (or local

server) in form of a datacube, where it is

stored, analyzed, and maintained locally.

Promoted as being relatively simple to install

and administer with reduced cost and

maintenance.


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Typical Architecture for MQE Tools


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MQE Tools - Development Issues

Architecture results in significant dataredundancy and may cause problems fornetworks that support many users.

Ability of each user to build a custom datacubemay cause a lack of data consistency amongusers.

Only a limited amount of data can beefficiently maintained.


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OLAP Extensions to SQL

SQL promoted as easy to learn, non-procedural, free-format, DBMS-independent,and international standard.

However, major disadvantage has been inabilityto represent many of the questions mostcommonly asked by business analysts.

IBM and Oracle jointly proposed OLAPextensions to SQL early in 1999, adopted as anamendment to SQL.


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OLAP Extensions to SQL - RISQL

Designed for business analysts.

Set of extensions that augments SQL with a

variety of powerful operations appropriate todata analysis and decision-support applications

such as ranking, moving averages,

comparisons, market share, this year versus

last year.


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Use of the RISQL CUME Function

Show the quarterly sales for branch office B003,along with the monthly year-to-date figures.

SELECT quarter, quarterlySales, CUME(quarterlySales)

AS Year-to-DateFROM BranchSales

WHERE branchNo = B003;


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Use of the RISQL MOVINGAVG / MOVINGSUM

Function

Show the first six monthly sales for branch office

B003 without the effect of seasonality.

SELECT month, monthlySales,

MOVINGAVG(monthlySales) AS 3-MonthMovingAvg,MOVINGSUM(monthlySales) AS 3-MonthMovingSum

FROM BranchSales

WHERE branchNo = B003;


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Data Mining

The process of extracting valid, previously

unknown, comprehensible, and actionable

information from large databases and using it

to make crucial business decisions (Simoudis,1996).

Involves analysis of data and use of software

techniques for finding hidden and unexpected

patterns and relationships in sets of data.


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Data Mining

Reveals information that is hidden andunexpected, as little value in finding patternsand relationships that are already intuitive.

Patterns and relationships are identified byexamining the underlying rules and features inthe data.

Tends to work from the data up and most

accurate results normally require largevolumes of data to deliver reliable conclusions.


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Data Mining

Starts by developing an optimal representationof structure of sample data, during which timeknowledge is acquired and extended to largersets of data.

Data mining can provide huge paybacks forcompanies who have made a significantinvestment in data warehousing.

Relatively new technology, however alreadyused in a number of industries.


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Examples of Applications of Data Mining

Retail / Marketing

Identifying buying patterns of customers.

Finding associations among customer

demographic characteristics.Predicting response to mailing campaigns.

Market basket analysis.


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Banking

Detecting patterns of fraudulent credit card

use.

Identifying loyal customers.

Predicting customers likely to change their

credit card affiliation.

Determining credit card spending bycustomer groups.


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Insurance

Claims analysis.

Predicting which customers will buy new policies.

Medicine

Characterizing patient behavior to predict surgery

visits.

Identifying successful medical therapies fordifferent illnesses.


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Data Mining Operations

Four main operations include: Predictive modeling.

Database segmentation.

Link analysis.

Deviation detection.

There are recognized associations between theapplications and the corresponding operations.

e.g. Direct marketing strategies use databasesegmentation.


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Data Mining Techniques

Techniques are specific implementations of the

data mining operations.

Each operation has its own strengths and

weaknesses.

Data mining tools sometimes offer a choice of

operations to implement a technique.


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Data Mining Techniques

Criteria for selection of tool includes

Suitability for certain input data types.

Transparency of the mining output.

Tolerance of missing variable values. Level of accuracy possible.

Ability to handle large volumes of data.

Data Mining Operations and Associated


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Data Mining Operations and Associated

Techniques


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Predictive Modeling

Similar to the human learning experience

uses observations to form a model of the important

characteristics of some phenomenon.

Uses generalizations of real world and abilityto fit new data into a general framework.

Can analyze a database to determine essential

characteristics (model) about the data set.


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Predictive Modeling

Model is developed using a supervised learning

approach, which has two phases: training and

testing.

Training builds a model using a large sample ofhistorical data called a training set.

Testing involves trying out the model on new,

previously unseen data to determine its accuracy

and physical performance characteristics.


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Predictive Modeling

Applications of predictive modeling include

customer retention management, credit

approval, cross selling, and direct marketing.

Two techniques associated with predictive

modeling: classification and value prediction,

distinguished by nature of the variable being

predicted.


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Predictive Modeling - Classification

Used to establish a specific predetermined class

for each record in a database from a finite set

of possible class values.

Two specializations of classification: tree

induction and neural induction.


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Example of Classification using Tree Induction

Example of Classification using Neural


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Example of Classification using Neural

Induction


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Predictive Modeling - Value Prediction

Used to estimate a continuous numeric value

that is associated with a database record.

Uses the traditional statistical techniques oflinear regression and nonlinear regression.

Relatively easy to use and understand.


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Linear regression attempts to fit a straight line

through a plot of the data, such that the line is

the best representation of the average of all

observations at that point in the plot.

Problem is that the technique only works well

with linear data and is sensitive to the presence

of outliers (i.e., data values, which do notconform to the expected norm).


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Although nonlinear regression avoids the main

problems of linear regression, still not flexible

enough to handle all possible shapes of the data

plot.

Statistical measurements are fine for building

linear models that describe predictable data

points, however, most data is not linear in

nature.


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Data mining requires statistical methods that

can accommodate non-linearity, outliers, and

non-numeric data.

Applications of value prediction include credit

card fraud detection or target mailing list

identification.


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Database Segmentation

Less precise than other operations thus lesssensitive to redundant and irrelevant features.

Sensitivity can be reduced by ignoring a subset

of the attributes that describe each instance orby assigning a weighting factor to eachvariable.

Applications of database segmentation includecustomer profiling, direct marketing, and crossselling.

Example of Database Segmentation using a


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Scatterplot


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Database Segmentation

Associated with demographic or neural

clustering techniques, distinguished by:

Allowable data inputs.

Methods used to calculate the distancebetween records.

Presentation of the resulting segments for

analysis.


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Link Analysis

Aims to establish links (associations) betweenrecords, or sets of records, in a database.

There are three specializations

Associations discovery.Sequential pattern discovery.

Similar time sequence discovery.

Applications include product affinity analysis,direct marketing, and stock price movement.


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Link Analysis - Associations Discovery

Finds items that imply the presence of other

items in the same event.

Affinities between items are represented by

association rules.

e.g. When customer rents property for more than 2

years and is more than 25 years old, in 40% of cases,

customer will buy a property. Association happens in

35% of all customers who rent properties.


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Link Analysis - Sequential Pattern Discovery

Finds patterns between events such that the

presence of one set of items is followed by

another set of items in a database of events

over a period of time. e.g. Used to understand long-term customer buying

behavior.

Link Analysis Similar Time Sequence


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Link Analysis - Similar Time Sequence

Discovery

Finds links between two sets of data that are

time-dependent, and is based on the degree of

similarity between the patterns that both time

series demonstrate.

e.g. Within three months of buying property, new

home owners will purchase goods such as cookers,

freezers, and washing machines.


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Deviation Detection

Relatively new operation in terms of

commercially available data mining tools.

Often a source of true discovery because itidentifies outliers, which express deviation

from some previously known expectation and

norm.


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Deviation Detection

Can be performed using statistics and

visualization techniques or as a by-product of

data mining.

Applications include fraud detection in the use

of credit cards and insurance claims, quality

control, and defects tracing.



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p S g g

Visualization


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Data Mining Tools

There are a growing number of commercialdata mining tools on the marketplace.

Important characteristics of data mining toolsinclude:

Data preparation facilities.

Selection of data mining operations.

Product scalability and performance. Facilities for visualization of results.


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Data Mining and Data Warehousing

Major challenge to exploit data mining is

identifying suitable data to mine.

Data mining requires single, separate, clean,integrated, and self-consistent source of data.


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A data warehouse is well equipped for

providing data for mining.

Data quality and consistency is a prerequisitefor mining to ensure the accuracy of the

predictive models. Data warehouses are

populated with clean, consistent data.


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Advantageous to mine data from multiplesources to discover as many interrelationships

as possible. Data warehouses contain data from

a number of sources.

Selecting relevant subsets of records and fields

for data mining requires query capabilities of

the data warehouse.


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Results of a data mining study are useful if

there is some way to further investigate the

uncovered patterns. Data warehouses provide

capability to go back to the data source.

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