SQL Server Analysis Services for Business Intelligence (BI Series)

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Building and Modifying an OLAP Cube

Designing a Unified Dimension Model (UDM)

Identifying measures and their suitable granularities

Adding new measure groups and creating custom measures

Creating dimensions

Implementing a Star and Snowflake Schema

Identifying role-play dimensions

Adding dimension attributes and properties

Configuring multilanguage support

Extending the Cube with Hierarchies

Creating hierarchies

Building natural hierarchies and creating attribute relationships

Discretising attribute values with the Clusters and Equal Areas algorithms

Parent-child relationships

Defining parent and key attributes

Generating level captions with Naming Template

Exploiting Advanced Dimension Relationships

Storing dimension data in fact tables

Building a degenerate dimension

Configuring fact relationships

Saving space with referenced dimension relationships

Identifying candidates for referenced relationships

Utilising the Dimension Usage tab to configure referenced relationships

Including dimensions with many-to-many relationships

Implementing intermediate measure groups and dimensions

Reporting on many-to-many dimensions without double counting

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Implementing a Tabular Model Database

Providing users with analytics via xVelocity and Power Pivot

Comparing DAX with MDX

Mapping out the role of SharePoint

Managing Cubes

Designing storage and aggregations

Choosing between ROLAP, MOLAP and HOLAP

Partitioning cubes for improved performance

Designing aggregations with Aggregation Design Wizard

Leveraging the Usage-Based Optimisation Wizard

Automating processing and deployment

Exploiting XMLA scripts and SSIS

Refreshing cubes with Proactive Caching

Performing Advanced Analysis with MDX

Retrieving data with MDX

Defining tuples, sets and calculated members

Querying cubes with MDX

Monitoring business performance with KPIs

Building goal, status and trend expressions

Using PARALLELPERIOD to compare past time periods

Simplifying KPI definitions using KPIValue and KPIGoal

Enhancing cubes with MDX

Adding runtime calculations to the cube

Adding drill-through and URL actions

Gaining Business Advantage with Data Mining

Determining the correct model

Identifying business tasks for data mining

Training and testing data-mining algorithms

Comparing algorithms with the accuracy chart

Performing real-world predictions

Classifying with Decision Trees, Neural Network and Naive Bayes algorithms

Predicting with the Time Series algorithm