Business Intelligence Fundamentals: Data Mining

ISV Innovation

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04/19/2023

ISV Innovation

Presented by

Business Intelligence Fundamentals: Data Mining

Ola EkdahlIT Mentors

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Business Intelligence Fundamentals: Data Mining 2

Agenda

1.Introducing Data Mining

2.Integration with SQL Server 2008 Components

3.Data Mining Programmability

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Where Are We?

Data Sources

Staging Area

Manual Cleansing

Data Marts

Data Warehouse


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Module Overview

Introducing Data Mining

Integration with SQL Server 2008 Components

Data Mining Programmability


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5Business Intelligence Fundamentals: Data Mining

Introducing Data Mining

Purpose of Data Mining

Business Scenarios

SQL Server 2008 Data Mining

Data Preparation

Data Mining Process

Data Mining Visualization

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Purpose of Data Mining

Addresses the problem of too much data and not enough information

Enables data exploration, pattern discovery, and pattern prediction—which lead to knowledge discovery

Forms a key part of a BI solution

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Business Scenarios Identifying responsive customers/unresponsive

customers (also known as churn analysis)

Detecting fraud

Targeting promotions

Managing risk

Forecasting sales

Cross-selling

Segmenting customers

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SQL Server 2008 Data Mining

Hides the complexity of an advanced technology

Includes full suite of algorithms to automatically extract information from data

Handles large volumes of data and complex data

Data can be sourced from relational and OLAP databases

Uses standard programming interfaces XMLA

DMX

Delivers a complete framework for building and deploying intelligent applications

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SQL Server 2008 Algorithms Decision Trees

The most popular data mining technique Used for classification

Clustering Finds natural groupings inside data

Sequence Clustering Groups a sequence of discrete events into natural groups

based on similarity Use this algorithm to understand how visitors use your Web

site


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SQL Server 2008 Algorithms Naïve Bayes

Used for classification in similar scenarios to Decision Trees

Linear Regression Finds the best possible straight line through a series of points Used for prediction analysis

Logistic Regression Fits to an exponential factor Used for prediction analysis


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SQL Server 2008 Algorithms Association Rules

Supports market basket analysis to learn what products are purchased together

Time Series Forecasting algorithm used for short-term or long-term

predictions future values from a time series Use multiple series to predict “what if” scenarios

Neural Network Used for classification and regression tasks More sophisticated than Decision Trees and Naïve Bayes, this

algorithm can explore extremely complex scenarios


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Data Preparation

Often significant amounts of effort are required to prepare data for mining Transforming for cleaning and reformatting

Isolating and flagging abnormal data

Appropriately substituting missing values

Discretizing continuous values into ranges

Normalizing values between 0 and 1


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Design time

Process time

Query time

Data Mining Process

Mining Model


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Design time

Process time

Query time

Data Mining Process

Mining Model

Training Data Data Mining Engine

Mar-2008Microsoft Developer & Platform Evangelism


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Design time

Process time

Query time

Data Mining Process

Data Mining Engine

Data to PredictPredicted Data

Mining Model

Mar-2008Microsoft Developer & Platform Evangelism


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Data Mining Visualization In contrast to OLTP and OLAP queries, data mining

queries typically extract previously unknown information

Visualizations can effectively present data discoveries

SQL Server 2008 provides algorithm-specific visualizations that you can se to Test and explore models in Business Intelligence

Development Studio

Embed into Windows Forms applications

Developers can construct and plug-in custom data mining viewers


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Integration with SQL Server 2008 Components Integration with SSIS

Integration with SSAS

Integration with SSRS


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Integration with SSIS

Perform data mining directly in the control flow or the data flow pipeline

Configure “intelligent” packages based on data mining query results

Enterprise Edition only


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Integration with SSAS

Create data mining models directly from OLAP stores

Create dimensions from data mining models to slice cubes using discovered patterns Decision Trees

Clustering

Association Rules


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Integration with SSRS

Present data mining results in SSRS reports Prediction queries

Content queries

Parameterized queries

Use a data mining query builder to easily select results

Apply grouping and aggregation to summarize results

Distribute data mining results by using subscriptions


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Data Mining Programmability

SSAS Data Mining Programmability Overview

Programming Interfaces

Embedding SSAS Data Mining

Extending SSAS Data Mining


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SSAS Data Mining Programmability Overview

Data Mining Interfaces

Analysis Server

OLAP Data Mining

Server ADOMD.NET

.NET Stored Procedures

Microsoft Algorithms

Third-Party Algorithms

WANXMLAOver TCP/IP

OLE DB ADO ADOMD.NET

XMLAOver HTTP

Any Platform, Any Device

C++ App VB App .NET App Any App

AMO


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Programming Interfaces

AMO (Analysis Management Objects) Administer database objects

Apply security

Manage processing

ADOMD.NET Connect to SSAS databases

Retrieve and manipulate data

Server ADOMD.NET Extend DMX by using .NET stored procedures

Mar-2008


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Embedding SSAS Data Mining

Validate or repair user entry

Integrate predictions Targeted advertising

“Those that bought this book also purchased these books”

Embed custom visualizations into Windows Forms applications to allow users to explore and understand model patterns

SSAS Data Mining ships with custom visualizations


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Extending SSAS Data Mining

Stored procedures

Enhanced Visual Studio data mining tools

Plug-in algorithms

Plug-in data mining viewers


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DEMO

Classifying Customers Likely to Purchase a Bicycle


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Resources www.microsoft.com/sql/technologies/dm

Links to technical resources, case studies, news, and reviews

www.sqlserverdatamining.com Site designed and maintained by the SQL Server Data Mining

team

Live samples

Tutorials

Webcasts

Tips and tricks

FAQ

Data Mining for SQL Server 2005, by ZhaoHui Tang and Jamie MacLennan


Documents

Business Intelligence Fundamentals: Data Mining