
Practical DMX Queries for Microsoft SQL Server Analysis Services 2008
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Content
- Cover Page
- Practical DMX Queries for Microsoft® SQL Server® Analysis Services 2008
- Copyright Page
- Contents
- Acknowledgments
- Introduction
- Chapter 1 Cases Queries
- Examining Source Data
- Flattened Nested Case Table
- Specific Source Columns
- Examining Training Data
- Examining Specific Cases
- Examining Test Cases
- Examining Model Cases Only
- Examining Another Model
- Expanding the Nested Table
- Sorting Cases
- Model and Structure Columns
- Specific Model Columns
- Distinct Column Values 1/2
- Distinct Column Values 2/2
- Cases by Cluster 1/4
- Cases by Cluster 2/4
- Cases by Cluster 3/4
- Cases by Cluster 4/4
- Content Query
- Decision Tree Cases
- Decision Tree Content
- Time Series Cases
- Sequence Clustering Cases 1/2
- Sequence Clustering Cases 2/2
- Neural Network and Naïve Bayes Cases
- Order By with Top
- Sequence Clustering Nodes 1/2
- Sequence Clustering Nodes 2/2
- Chapter 2 Content Queries
- Content Query
- Updating Cluster Captions
- Content with New Caption
- Changing Caption Back
- Content Columns
- Node Type
- Flattened Content
- Flattened Content with Subquery
- Subquery Columns
- Subquery Column Aliases
- Subquery Where Clause
- Individual Cluster Analysis
- Demographic Analysis
- Renaming Clusters
- Querying Renamed Clusters
- Clusters with Predictable Columns
- Narrowing Down Content
- Flattening Content Again
- Some Tidying Up
- More Tidying Up
- Looking at Bike Buyers
- Who Are the Best Customers?
- How Did All Customers Do?
- Decision Tree Content
- Decision Tree Node Types
- Decision Tree Content Columns
- Flattened Column
- Honing the Result
- Just the Bike Buyers
- Tidying Up
- VBA in DMX
- Association Content
- Market Basket Analysis
- Naïve Bayes Content
- Naïve Bayes Node Type
- Flattening Naïve Bayes Content
- Naïve Bayes Content Subquery 1/2
- Naïve Bayes Content Subquery 2/2
- Chapter 3 Prediction Queries with Decision Trees
- Select on Mining Model 1/6
- Select on Mining Model 2/6
- Select on Mining Model 3/6
- Select on Mining Model 4/6
- Select on Mining Model 5/6
- Select on Mining Model 6/6
- Prediction Query
- Aliases and Formatting
- Natural Prediction Join
- More Demographics
- Natural Prediction Join Broken
- Natural Prediction Join Fixed
- Nonmodel Columns
- Ranking Probabilities
- Predicted Versus Actual
- Bike Buyers Only
- More Demographics
- Choosing Inputs 1/3
- Choosing Inputs 2/3
- Choosing Inputs 3/3
- All Inputs and All Customers
- Singletons 1/6
- Singletons 2/6
- Singletons 3/6
- Singletons 4/6
- Singletons 5/6
- Singletons 6/6
- New Customers
- New Bike-Buying Customers
- A Cosmetic Touch
- PredictHistogram() 1/2
- PredictHistogram() 2/2
- Chapter 4 Prediction Queries with Time Series
- Analyzing All Existing Sales
- Analyzing Existing Sales by Category
- Analyzing Existing Sales by Specific Periods-Lag() 1/3
- Analyzing Existing Sales by Specific Periods-Lag() 2/3
- Analyzing Existing Sales by Specific Periods-Lag() 3/3
- PredictTimeSeries() 1/11
- PredictTimeSeries() 2/11
- PredictTimeSeries() 3/11
- PredictTimeSeries() 4/11
- PredictTimeSeries() 5/11
- PredictTimeSeries() 6/11
- PredictTimeSeries() 7/11
- PredictTimeSeries() 8/11
- PredictTimeSeries() 9/11
- PredictTimeSeries() 10/11
- PredictTimeSeries() 11/11
- PredictStDev()
- What-If 1/3
- What-If 2/3
- What-If 3/3
- Chapter 5 Prediction and Cluster Queries with Clustering
- Cluster Membership 1/3
- Cluster Membership 2/3
- Cluster Membership 3/3
- ClusterProbability() 1/2
- ClusterProbability() 2/2
- Clustering Parameters
- Another ClusterProbability
- Cluster Content 1/2
- Cluster Content 2/2
- PredictCaseLikelihood() 1/3
- PredictCaseLikelihood() 2/3
- PredictCaseLikelihood() 3/3
- Anomaly Detection
- Cluster with Predictable Column 1/3
- Cluster with Predictable Column 2/3
- Cluster with Predictable Column 3/3
- Clusters and Predictions
- Chapter 6 Prediction Queries with Association and Sequence Clustering
- Association Content-Item Sets
- Association Content-Rules
- Important Rules
- Twenty Most Important Rules
- Particular Product Models
- Another Product Model
- Nested Table
- PredictAssociation()
- Cross-Selling Prediction 1/7
- Cross-Selling Prediction 2/7
- Cross-Selling Prediction 3/7
- Cross-Selling Prediction 4/7
- Cross-Selling Prediction 5/7
- Cross-Selling Prediction 6/7
- Cross-Selling Prediction 7/7
- Sequence Clustering Prediction 1/3
- Sequence Clustering Prediction 2/3
- Sequence Clustering Prediction 3/3
- Chapter 7 Data Definition Language (DDL) Queries
- Creating a Mining Structure
- Creating a Mining Model
- Training a Mining Model
- Structure Cases
- Model Cases
- Model Content
- Model Predict
- Specifying Structure Holdout
- Specifying Model Parameter
- Specifying Model Filter
- Specifying Model Drill-through
- Training the New Models
- Cases-with No Drill-through
- Cases-with Drill-through
- Structure with Holdout
- Specifying Model Parameter, Filter, and Drill-through
- Training New Model
- Unprocessing a Structure
- Model Cases with Filter and Drill-through
- Clearing Out Cases
- Removing Models
- Removing Structures
- Renaming a Model
- Renaming a Structure
- Making Backups
- Removing the Backed-up Structure
- Restoring a Backup
- Structure with Nested Case Table
- Model Using Nested Case Table
- Model Training with Nested Case Table
- Prediction Queries with Nested Cases 1/2
- Prediction Queries with Nested Cases 2/2
- Cube-Mining Structure
- Cube-Mining Model
- Cube-Model Training
- Cube-Structure Cases
- Cube-Model Content
- Cube-Model Prediction
- Chapter 8 Schema and Column Queries
- DMSCHEMA_MINING_SERVICES 1/2
- DMSCHEMA_MINING_SERVICES 2/2
- DMSCHEMA_MINING_SERVICE_PARAMETERS 1/2
- DMSCHEMA_MINING_SERVICE_PARAMETERS 2/2
- DMSCHEMA_MINING_MODELS 1/3
- DMSCHEMA_MINING_MODELS 2/3
- DMSCHEMA_MINING_MODELS 3/3
- DMSCHEMA_MINING_COLUMNS 1/3
- DMSCHEMA_MINING_COLUMNS 2/3
- DMSCHEMA_MINING_COLUMNS 3/3
- DMSCHEMA_MINING_MODEL_CONTENT 1/5
- DMSCHEMA_MINING_MODEL_CONTENT 2/5
- DMSCHEMA_MINING_MODEL_CONTENT 3/5
- DMSCHEMA_MINING_MODEL_CONTENT 4/5
- DMSCHEMA_MINING_MODEL_CONTENT 5/5
- DMSCHEMA_MINING_FUNCTIONS 1/3
- DMSCHEMA_MINING_FUNCTIONS 2/3
- DMSCHEMA_MINING_FUNCTIONS 3/3
- DMSCHEMA_MINING_STRUCTURES 1/2
- DMSCHEMA_MINING_STRUCTURES 2/2
- DMSCHEMA_MINING_STRUCTURE_COLUMNS 1/3
- DMSCHEMA_MINING_STRUCTURE_COLUMNS 2/3
- DMSCHEMA_MINING_STRUCTURE_COLUMNS 3/3
- DMSCHEMA_MINING_MODEL_XML 1/2
- DMSCHEMA_MINING_MODEL_CONTENT_PMML
- DMSCHEMA_MINING_MODEL_XML 2/2
- Discrete Model Columns 1/5
- Discrete Model Columns 2/5
- Discrete Model Columns 3/5
- Discrete Model Columns 4/5
- Discrete Model Columns 5/5
- Discretized Model Column
- Discretized Model Column-Minimum
- Discretized Model Column-Maximum
- Discretized Model Column-Mid Value
- Discretized Model Column-Range Values
- Discretized Model Column-Spread
- Continuous Model Column-Spread
- Chapter 9 After You Finish
- Where to Use DMX
- SSRS
- SSIS
- SQL
- XMLA
- Winforms and Webforms
- Third-Party Software
- Copy and Paste
- Appendix A Graphical Content Queries
- Content Queries
- Graphical Content Queries in SSMS
- Clustering Model
- Time Series Model
- Association Rules Model
- Decision Trees Model
- Graphical Content Queries in Excel 2007
- Data Mining Ribbon
- Table Tools/Analyze Ribbon
- Graphical Content Queries in BIDS
- Opening the Adventure Works Solution
- Reverse-Engineering the Adventure Works Database
- Adventure Works Database in Connected Mode
- Viewing Content
- Tracing Generated DMX
- Excel Data Mining Functions
- Appendix B Graphical Prediction Queries
- Prediction Queries
- SSMS Prediction Queries
- SSRS Prediction Queries
- SSIS Prediction Queries
- Control Flow
- Data Flow
- SSAS Prediction Queries
- Building a Prediction Query
- Clustering Prediction Queries
- Time Series Prediction Queries
- Association Prediction Queries
- Decision Trees Prediction Queries
- Excel Prediction Queries
- Excel Data Mining Functions
- Appendix C Graphical DDL Queries
- DDL Queries
- SSAS in BIDS
- Excel 2007/2010
- SSIS in BIDS
- Index
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