SQL Server 2008 Data Mining

SSAS 2008 Data Mining
Lynn Langit MSDN Developer Evangelist – Southern California
http://blogs.msdn.com/SoCalDevGal

Session Prerequisites
Working SQL Server 2005 Developer
Understanding of OLAP concepts
Working SQL Server Analysis Server 2005 Developer
Interest in or basic knowledge of Data Mining concepts

Session Objectives and Agenda
Understand what, why, when & how of SQL Server 2008 Data Mining
Examine the core functionality of the Data Mining Extensions
Hear about the new and/or advanced functionality of Data Mining

What and Why Data Mining? Predictive Analytics Presentation Exploration Discovery Passive Interactive Proactive Role of Software Business Insight Canned reporting Ad-hoc reporting OLAP Data mining

OLAP vs. Data Mining

DM - From Scenarios to Tasks

From Tasks to Techniques

BI for Everyone Enterprise – Performance Point Individual – Excel Project – Share Point

Microsoft’s Predictive Analytics Data Mining SQL extensions (DMX) Application Developer Data Mining Specialist Microsoft Dynamics CRM Analytics Foundation SQL Server 2005 Business Intelligence Development Studio Microsoft SQL Server 2008 Analysis Services Information Worker Data Mining Add-ins for the 2007 Microsoft Office system Microsoft SQL Server 2008 Data Mining BI Analyst Custom Algorithms

Data Mining Add-ins for Office 2007 Table Analysis Tools for Excel 2007 Data Mining Template for Visio 2007 Data Mining Client for Excel 2007 Information Worker BI Analyst Data Mining Specialist

Microsoft Data Mining Lifecycle CRISP-DM SSAS (Data Mining) Excel SSAS (DSV) Query Excel SSIS SSAS SSRS Excel Your Apps SSIS SSAS Excel Data www.crisp-dm.org Business Understanding Data Understanding Data Preparation Modeling Evaluation Deployment

Understand & Prepare specifics

Demo 1 – Explore / Clean / Partition Data

Modeling Specifics

Demo 3 – Modeling

Evaluation Specifics

Demo 4 – Evaluation

Data Mining – Logical Model Mining Model Mining Model Training Data DB data Client data Application data Data Mining Engine Data To Predict Predicted Data Mining Model DB data Client data Application data “ Just one row ” Data Mining Engine

Data Mining - Physical Model Analysis Services Server Mining Model Data Mining Algorithm Data Source Your Application OLE DB/ ADOMD/ XMLA Deploy BI Dev Studio (Visual Studio) App Data

Data Mining Interfaces – APIs XMLA Over TCP/IP XMLA Over HTTP Analysis Server (msmdsrv.exe) OLAP Data Mining Server ADOMD.NET .Net Stored Procedures Microsoft Algorithms Third Party Algorithms OLEDB for OLAP/DM ADO/DSO Any Platform, Any Device C++ App VB App .Net App AMO Any App ADOMD.NET WAN DM Interfaces

Configuration
Model Creation/Management
Database Administrators
Session Mining Models
Model Application
Permissions on models
Permissions on data sources

Deployment
Browse
Copy to Excel
Drillthrough
Query
Default
Advanced
Excel Services
Manage models and structures
Export/Import
Rename
Connection
Database
Trace

Excel Functions*
DMPREDICTTABLEROW ( Connection, ModelName, PredictionResult, TableRowRange [, string CommaSeparatedColumnNames] )
DMPREDICT ( Connection, Model, PredictionResult, Value1, Name1, [...,Value32, Name32] )
DMCONTENTQUERY (Connection, Model, PredictionResult [, WhereClause])

Data Mining Extensions (DMX) CREATE MINING MODEL CreditRisk (CustID LONG KEY, Gender TEXT DISCRETE, Income LONG CONTINUOUS, Profession TEXT DISCRETE, Risk TEXT DISCRETE PREDICT) USING Microsoft_Decision_Trees INSERT INTO CreditRisk (CustId, Gender, Income, Profession, Risk) Select CustomerID, Gender, Income, Profession,Risk From Customers Select NewCustomers.CustomerID, CreditRisk.Risk, PredictProbability(CreditRisk.Risk) FROM CreditRisk PREDICTION JOIN NewCustomers ON CreditRisk.Gender=NewCustomer.Gender AND CreditRisk.Income=NewCustomer.Income AND CreditRisk.Profession=NewCustomer.Profession

DMX Column Expressions
Predictable Columns
Source Data Columns
Functions
Predict
“ Workhorse”
Discrete scalar values
Continuous scalar values
Associative nested tables
Sequence nested tables
Time Series
Overloaded to
PredictAssociation
PredictSequence
PredictTimeSeries
PredictProbability
PredictSupport
PredictHistogram
Cluster
ClusterProbability
GetNodeId
IsInNode
Arithmetic operators
Stored Procedure
Subselect
Select from nested tables

Data Mining Interfaces – XMLA ++ XMLA Over TCP/IP XMLA Over HTTP Analysis Server (msmdsrv.exe) OLAP Data Mining Server ADOMD.NET .Net Stored Procedures Microsoft Algorithms Third Party Algorithms OLEDB for OLAP/DM ADO/DSO Any Platform, Any Device C++ App VB App .Net App AMO Any App ADOMD.NET WAN DM Interfaces

New to SQL Server 2008
Microsoft_Time_Series algorithm improved
now includes ARIMA in addition to the existing ARTxp method, and a blending algorithm is now used to deliver more accurate and stable predictions, both short and long term, from a hybrid model. In addition, a new prediction mode allows you to add new data to time series models.
Holdout Support added
You can easily partition your data into training and test sets that are stored in the mining structure and are available to query after processing.
Ability to build mining models based on filtered subsets added
(e.g. just male customers), this means that you no longer have to create multiple mining structures and re-read the source data for such variations over a dataset
Drillthrough functionality extended
makes all mining structure columns available, not just columns included in the model
allows you to build more compact models without sacrificing the ability to producing actionable output reports like targeted mailing lists.
Cross-validation added
allows users to quickly validate their modeling approach by automatically building temporary models and evaluating accuracy measures across K folds. The feature is available through a new cross-validation tab under Accuracy Charts in Business Intelligence Development Studio, in addition to being accessible programmatically via a stored procedure call.

Summary
Data Mining in SQL Server 2008 is mature, powerful and accessible
Can use Excel 2007
Familiar client for BI – OLAP cubes AND Data Mining models
Model Creators / Users
Excel Data or Server Data
SSAS and Excel both support the full DM Cycle
Data Understanding
Data Preparation
Modeling
Validation
Deployment

DM Webcasts Fri, 02 Nov 2007 MSDN Webcast: Build Smart Web Applications with SQL Server Data Mining (Level 200) Thu, 08 Nov 2007 MSDN Webcast: Building Adaptive Applications with SQL Server Data Mining (Level 300) Mon, 19 Nov 2007 MSDN Webcast: Extending and Customizing SQL Server Data Mining (Level 300) Fri, 30 Nov 2007 MSDN Webcast: Creating Visualizations for SQL Server Data Mining (Level 300) Thu, 01 Nov 2007 TechNet Webcast: Deliver Actionable Insight Throughout Your Organization with Data Mining (Part 1 of 3): Your First Project with SQL Server Data Mining (Level 200) Thu, 15 Nov 2007 TechNet Webcast: Deliver Actionable Insight Throughout Your Organization with Data Mining (Part 2 of 3): Understand SQL Server Data Mining Add-ins for the 2007 Office System (Level 200) Thu, 29 Nov 2007 TechNet Webcast: Deliver Actionable Insight Throughout Your Organization with Data Mining (Part 3 of 3): Use Predictive Intelligence to Create Smarter KPIs (Level 200)

BI Resources from Lynn Langit 1. “Foundations of SQL Server 2005 Business Intelligence” (published by APress in April 2007) 2. http://blogs.msdn.com/SoCalDevGal 3. “Building Business Intelligence Solutions with SQL Server 2008” (MSPress Fall 2008)

DM Resources Technical Communities, Webcasts, Blogs, Chats & User Groups http://www.microsoft.com/communities/default.mspx Microsoft Developer Network (MSDN) & TechNet http://microsoft.com/msdn http://microsoft.com/technet Trial Software and Virtual Labs http://www.microsoft.com/technet/downloads/trials/default.mspx Microsoft Learning and Certification http://www.microsoft.com/learning/default.mspx SQL Server Data Mining http://www.sqlserverdatamining.com http://www.microsoft.com/bi/bicapabilities/data-mining.aspx

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SQL Server 2008 Data Mining

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