SQL Server: Data Mining

100 SQL SERVER: Data Mining

Types of analysis • Ad-hoc query/Reporting/Analysis – What is the purpose? • Simple reports • Key Performance Indicators • OLAP cubes – Slice & Dice – In Real time - What happens now? • Events/Triggers • Data Mining – How do we do it? – What happens?

What does Data Mining Do? Explores Your Data Finds Patterns Performs Predictions

Data Mining Algorithms • Classification • Regression • Segmentation • Association • Forecasting • Text Analysis • Advanced Data Exploration

Mining Process Training data Data to be Mining Model predicted DM Engine Mining Model With predictions

Data Mining Process SSAS (OLAP) Business Data DSV Understanding Understanding SSIS SSAS Data Data (OLAP) Preparation SSIS SSAS(OLAP) SSRS Deployment Flexible APIs SSAS Modeling (Data Mining) Evaluation www.crisp-dm.org

Data Mining in SQL Server 2008 • New algorithms developed in conjunction with Microsoft Research • Data mining is made accessible and easy to use through integrated user interface, cross- product integration and familiar, standard APIs • Complete framework for building and deploying intelligent applications on the fly • Integration into the cloud.

Top New Features in SQL Server 2008 • Test multiple data mining models simultaneously with statistical scores of error and accuracy and confirm their stability with cross validation • Build multiple, incompatible mining models within a single structure; apply model analysis over filtered data; query against structure data to present complete information, all enabled by enhanced mining structures • Combine the best of both worlds by blending optimized near-term predictions (ARTXP) and stable long-term predictions (ARIMA) with Better Time Series Support • Discover the relationship between items that are frequently purchased together by using Shopping Basket Analysis; generate interactive forms for scoring new cases with Predictive Calculator, delivered with Microsoft SQL Server 2008 Data Mining Add-ins for Office 2007

Rich and Innovative Algorithms • Benefit from many rich and innovative data mining algorithms, most developed by Microsoft Research to support common business problems promptly and accurately. • Market Basket Analysis - Discover which items tend to be bought together to create recommendations on- the-fly and to determine how product placement can directly contribute to your bottom line • Churn Analysis - Anticipate customers who may be considering canceling their service and identify benefits that will keep them from leaving • Market Analysis - Define market segments by automatically grouping similar customers together. Use these segments to seek profitable customers • Forecasting - Predict sales and inventory amounts and learn how they are interrelated to foresee bottlenecks and improve performance • Data Exploration - Analyze profitability across customers, or compare customers who prefer different brands of the same product to discover new opportunities • Unsupervised Learning - Identify previously unknown relationships between various elements of your business to better inform your decisions • Web Site Analysis - Understand how people use your Web site and group similar usage patterns to offer a better experience • Campaign Analysis - Spend marketing dollars more effectively by targeting the customers most likely to respond to a promotion • Information Quality - Identify and handle anomalies during data entry or data loading to improve the quality of information • Text Analysis - Analyze feedback to find common themes and trends that concern your customers or employees, informing decisions with unstructured input

Value of Data Mining Business Knowledge SQL Server 2008 Business value Data Mining OLAP Reports (Adhoc) Reports (static) Simple Complex Usability

Data Mining User Interface • SQL Server BI Development Studio – Environment for creation and data exploration – Data Mining projects in Visual Studio solutions, tightly integrated – Source Control Integration • SQL Server Management Studio – One tool for all administrative tasks – Manage, view and query mining models

BI Integration • Integration Services – Data Mining processing and results integrate directly in IS pipeline • OLAP – Processing of mining models directly from cubes – Use of mining results as dimensions • Reporting Services – Embed Data Mining results directly in Reporting Services Reports

Applied Data Mining • Make Decisions without Coding – Learn business rules directly from data • Client Customization – Learn logic customized for each client • Automatic Update – Data mining application logic updated by model re- processing – Applications do not need to be rewritten, recompiled, re- deployed

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

Data Mining EXtensions • OLE DB for Data Mining specification – Now part of XML/A specification – See www.xmla.org for XML/A details • Connect to Analysis Server – OLEDB, ADO, ADO.Net, ADOMD.Net, XMLA Dim cmd as ADOMD.Command Dim reader as ADOMD.DataReader Cmd.Connection = conn Set reader = Cmd.ExecuteReader(“Select Predict(Gender)…”)

Typical DM Process Using DMX Define a model: CREATE MINING MODEL …. Data Mining Train a model: Management System INSERT INTO dmm …. (DMMS) Training Data Prediction using a model: Mining Model SELECT … FROM dmm PREDICTION JOIN … Prediction Input Data

DMX Commands • Definition (DDL) – CREATE – Make new model – SELECT INTO – Create model by copying existing – EXPORT – Save model as .abf file – IMPORT – Retrieve model from .abf file • Manipulation (DML) – INSERT INTO – Train model – UPDATE – Change content of model – DELETE – Clear content – SELECT – Browse model

DMX SELECT Elements • SELECT [FLATTENED] [TOP] <columns> • FROM <model> • PREDICTION JOIN <table> • ON <mapping> • WHERE <filter> • ORDER BY <sort expression> – Use query builder to create SELECT statement

Training a DM Model: Simple INSERT INTO CollegePlanModel (StudentID, Gender, ParentIncome, Encouragement, CollegePlans) OPENROWSET(‘<provider>’, ‘<connection>’, ‘SELECT StudentID, Gender, ParentIncome, Encouragement, CollegePlans FROM CollegePlansTrainData’)

Prediction Using a DM Model • PREDICTION JOIN SELECT t.ID, CPModel.Plan FROM CPModel PREDICTION JOIN OPENQUERY(…,„SELECT * FROM NewStudents‟) AS t ON CPModel.Gender = t.Gender AND CPModel.IQ = t.IQ

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SQL Server: Data Mining

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