bi 2008 simple
DESCRIPTION
introduction to BI for .NET developersTRANSCRIPT
Business Intelligence
Lynn Langit/MSDN Developer Evangelist Southern California
http://blogs.msdn.com/SoCalDevGal
Predictive AnalyticsPredictive Analytics
PresentatioPresentationn
ExplorationExploration DiscoveryDiscovery
PassivePassive
InteractiInteractiveve
ProactivProactivee
Role of SoftwareRole of Software
Business Business InsightInsight
Canned reportingCanned reporting
Ad-hoc reportingAd-hoc reporting
OLAPOLAP
Data miningData mining
What and Why BI?
SQL Server 2008 BI & Tools OLTP – SQL Server Engine
SSMS / Profiler and other mgmt tools Reporting – SSRS
No need for IIS, BIDS / Report Manager to design
Integrates with SharePoint ETL – SSIS
Part of SQL Server, BIDS to design OLAP – SSAS
Multidimensional Cubes, BIDS / SSMS Data Mining – SSAS
Algorithm-based models – BIDS / Excel / SSMS
Why BI? Faster reports
OLAP can be 1,000% faster Flexible
click to query using pivot tables, add calculated members, create custom views
Proactive ‘discover’ patterns in data, ‘predict’ future
Reduce load on OLTP source systems
Scalable no manual index tuning, data de-normalization
SQL Server 2008 Languages OLTP – SQL Server Engine
T-SQL, .NET (CLR), XML Reporting – SSRS
RDL + queries ETL – SSIS
XMLA metadata + queries, .NET extendable
OLAP – SSAS MDX, XMLA
Data Mining – SSAS DMX, XMLA, PMML
Cubes vs. Data Mining
Where do I start? Understand OLAP modeling
Star schema + grain statements Review AdventureWorks DW sample
From www.CodePlex.com Realistically access source data quality
Plan for ETL, learn SSIS Leverage Excel
Light-weight data mining designer and client
OLAP cube pivot table client
Demo 1 – SSAS Cubes
Data Mining Add-ins for Office 2007
Table Analysis Tools for Excel 2007Table Analysis Tools for Excel 2007
Data Mining Template for Visio 2007Data Mining Template for Visio 2007
Data Mining Client for Excel 2007Data Mining Client for Excel 2007
Information Information WorkerWorker
BI AnalystBI Analyst
Data Mining Data Mining SpecialistSpecialist
Demo – Data Mining
DM - From Scenarios to Tasks
From Tasks to Techniques
Understand & Prepare specifics
Modeling Specifics
New to SQL Server 2008 Microsoft Time Series algorithm improved
ARIMA plus ARTxp method, and a blending algorithm = better results
New prediction mode allows adding new data to time series models
Holdout Support added Easily partition data into training and test sets that are stored in mining structure & available to
query after processing
Ability to build mining models based on filtered subsets added Results in less structures, i.e. can just filter existing
Drillthrough functionality extended makes all mining structure columns available, not just columns included in the model
allows you to build more compact models
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 BIDS, 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 2007MSDN Webcast: Build Smart Web Applications with SQL Server Data Mining (Level 200)Thu, 08 Nov 2007MSDN Webcast: Building Adaptive Applications with SQL Server Data Mining (Level 300)Mon, 19 Nov 2007MSDN Webcast: Extending and Customizing SQL Server Data Mining (Level 300)Fri, 30 Nov 2007MSDN Webcast: Creating Visualizations for SQL Server Data Mining (Level 300)Thu, 01 Nov 2007TechNet 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 2007TechNet 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 2007TechNet 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)
2.2. http://blogs.msdn.com/SoCalDevGal
3.3. “Building Business Intelligence “Building Business Intelligence Solutions with SQL Server 2008” Solutions with SQL Server 2008” (MSPress Fall 2008)(MSPress Fall 2008)
DM Resources
Technical Communities, Webcasts, Blogs, Chats & User Groupshttp://www.microsoft.com/communities/default.mspx
Microsoft Developer Network (MSDN) & TechNet http://microsoft.com/msdn http://microsoft.com/technet
Trial Software and Virtual Labshttp://www.microsoft.com/technet/downloads/trials/default.mspx
Microsoft Learning and Certificationhttp://www.microsoft.com/learning/default.mspx
SQL Server Data Mininghttp://www.sqlserverdatamining.comhttp://www.microsoft.com/bi/bicapabilities/data-mining.aspx
END – END - END End of first set
Business Intelligence
Lynn Langit/MSDN Developer Evangelist Southern California
http://blogs.msdn.com/SoCalDevGal
Session Prerequisites – Session Two 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 Two Objectives and Agenda Understand what’s new SQL Server 2008
Business Intelligence SSAS OLAP cubes SSAS Data Mining Structures
Demo – Simplified Cube / Dim Wizards
Demo – New Aggregation Designer
Data Mining Are you using it now?
Data Mining – Logical Model
Mining ModelMining Model
Mining ModelMining Model
Training DataTraining Data
DB dataDB dataClient dataClient dataApplication dataApplication data
Data MiningData MiningEngineEngine
To To PredictPredict
Predicted DataPredicted Data
Mining ModelMining ModelDB dataDB dataClient dataClient dataApplication dataApplication data““Just one rowJust one row””
Data MiningData MiningEngineEngine
algorithmalgorithm
Evaluation Specifics
Analysis ServicesAnalysis ServicesServerServer
Mining ModelMining Model
Data Mining AlgorithmData Mining Algorithm DataDataSourceSource
Data Mining - Physical Model
Your ApplicationYour Application
OLE DB/ ADOMD/ XMLAOLE DB/ ADOMD/ XMLA
DeploDeployy
BI Dev BI Dev StudioStudio (Visual (Visual Studio)Studio)
App DataApp Data
Data Mining Interfaces – APIs
Analysis Server (msmdsrv.exe)
OLAP Data Mining
Server ADOMD.NET
.Net Stored Procedures Microsoft Algorithms Third Party Algorithms
XMLAXMLAOver TCP/IPOver TCP/IP
OLEDB for OLAP/DM ADO/DSO
XMLAXMLAOver HTTPOver HTTP
Any Platform, Any Device
C++ App VB App .Net App
AMO
Any App
ADOMD.NET
WANWAN
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 MODELCREATE MINING MODEL CreditRiskCreditRisk
(CustID(CustID LONG KEY, LONG KEY,
Gender TEXT DISCRETE,Gender TEXT DISCRETE,
Income Income LONG LONG CONTINUOUS,CONTINUOUS,
Profession TEXT DISCRETE,Profession TEXT DISCRETE,
RiskRisk TEXT DISCRETE PREDICT) TEXT DISCRETE PREDICT)
USINGUSING Microsoft_Decision_Trees Microsoft_Decision_Trees
INSERT INTOINSERT INTO CreditRisk CreditRisk
(CustId, Gender, Income, (CustId, Gender, Income, Profession, Risk)Profession, Risk)
Select Select
CustomerID, Gender, Income, CustomerID, Gender, Income, Profession,RiskProfession,Risk
From CustomersFrom Customers
SelectSelect NewCustomers.CustomerID, NewCustomers.CustomerID, CreditRisk.Risk, CreditRisk.Risk, PredictProbability(CreditRisk.Risk)PredictProbability(CreditRisk.Risk)
FROMFROM CreditRisk CreditRisk PREDICTION JOINPREDICTION JOIN NewCustomersNewCustomers
ONON CreditRisk.Gender=NewCustomer.GenderCreditRisk.Gender=NewCustomer.Gender
ANDAND CreditRisk.Income=NewCustomer.Income CreditRisk.Income=NewCustomer.Income
AND AND CreditRisk.Profession=NewCustomer.ProfessionCreditRisk.Profession=NewCustomer.Profession
DMX Column Expressions Predictable Columns Source Data Columns Functions - Predict
“Workhorse”Discrete scalar valuesContinuous scalar valuesAssociative nested tablesSequence nested tablesTime SeriesOverloaded to
PredictAssociationPredictSequencePredictTimeSeries
PredictProbability PredictSupport PredictHistogram Cluster ClusterProbability GetNodeId IsInNode
Arithmetic operators Stored Procedure Subselect
Select from nested tables
Data Mining Interfaces – XMLA ++
Analysis Server (msmdsrv.exe)
OLAP Data Mining
Server ADOMD.NET
.Net Stored Procedures Microsoft Algorithms Third Party Algorithms
XMLAXMLAOver TCP/IPOver TCP/IP
OLEDB for OLAP/DM ADO/DSO
XMLAXMLAOver HTTPOver HTTP
Any Platform, Any Device
C++ App VB App .Net App
AMO
Any App
ADOMD.NET
WANWAN
DM Interfaces
New to SQL Server 2008 Microsoft Time Series algorithm improved
ARIMA plus ARTxp method, and a blending algorithm = better results
New prediction mode allows adding new data to time series models
Holdout Support added Easily partition data into training and test sets that are stored in mining structure & available to
query after processing
Ability to build mining models based on filtered subsets added Results in less structures, i.e. can just filter existing
Drillthrough functionality extended makes all mining structure columns available, not just columns included in the model
allows you to build more compact models
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 BIDS, 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 2007MSDN Webcast: Build Smart Web Applications with SQL Server Data Mining (Level 200)Thu, 08 Nov 2007MSDN Webcast: Building Adaptive Applications with SQL Server Data Mining (Level 300)Mon, 19 Nov 2007MSDN Webcast: Extending and Customizing SQL Server Data Mining (Level 300)Fri, 30 Nov 2007MSDN Webcast: Creating Visualizations for SQL Server Data Mining (Level 300)Thu, 01 Nov 2007TechNet 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 2007TechNet 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 2007TechNet 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)
2.2. http://blogs.msdn.com/SoCalDevGalhttp://blogs.msdn.com/SoCalDevGal
3.3. “Building Business Intelligence “Building Business Intelligence Solutions with SQL Server 2008” Solutions with SQL Server 2008” (MSPress Fall 2008)(MSPress Fall 2008)
DM Resources
Technical Communities, Webcasts, Blogs, Chats & User Groupshttp://www.microsoft.com/communities/default.mspx
Microsoft Developer Network (MSDN) & TechNet http://microsoft.com/msdn http://microsoft.com/technet
Trial Software and Virtual Labshttp://www.microsoft.com/technet/downloads/trials/default.mspx
Microsoft Learning and Certificationhttp://www.microsoft.com/learning/default.mspx
SQL Server Data Mininghttp://www.sqlserverdatamining.comhttp://www.microsoft.com/bi/bicapabilities/data-mining.aspxhttp://www.microsoft.com/bi/bicapabilities/data-mining.aspx