SQL Server 2005 Analysis Services | MSBI Tutorial pdf

SQL Server 2005 Analysis Services

By creating an easy-to-use, extensible, accessible, and flexible platform, SQL Server 2005 Analysis Services (SSAS) data mining capabilities introduce data mining to organizations that previously would never have considered a data mining solution. SSAS improves access to critical, timely business data. Using multidimensional storage, SSAS provides rapid, sophisticated analysis of large and complex datasets. Through an enterprise-class architecture; a deep integration with the SQL Server family of BI tools; and a rich set of tools, APIs, and algorithms, SSAS helps provide customized data-driven solutions to a broad range of business problems.

Data Mining Algorithms
SSAS provides multiple algorithms for use in your data mining solutions. These algorithms are a subset of all the algorithms that can be used for data mining. 

They include the following:
=> Microsoft Naive Bayes Algorithm
=> Microsoft Association Algorithm
=> Microsoft Sequence Clustering Algorithm
=> Microsoft Time Series Algorithm
=> Microsoft Neural Network Algorithm
=> Microsoft Logistic Regression Algorithm
=> Microsoft Decision Trees Algorithm Enhancements
=> Microsoft Linear Regression Algorithm
Different algorithms are preferred for different goals and each algorithm can be used for multiple problems. Because each model returns a different type of result, SSAS provides a separate viewer for each algorithm. Third-party independent software vendors (ISVs) can develop algorithms that smoothly fit into the SSAS data mining framework.

Microsoft Business Intelligence in Action
Claus is thinking about company expansion, but he doesn’t know if the additional airfares and routes would in time pay for the extra airplanes. He uses the Data Mining Wizard in SSAS to add a new mining structure. By using the Microsoft Time Series Algorithm on historical data from the past five years, he can forecast future sales with and without expansion. The results help him conclude that the overhead brought by expansion at this time would minimize any future profits.

Unified Dimensional Modeling
Combining the best aspects of traditional OLAP analysis and relational reporting, SSAS offers a central metadata repository, the Unified Dimensional Model (UDM), which defines business entities, business logic, calculations, and metrics. The UDM also serves as the single data source for all reports, spreadsheets, OLAP browsers, KPIs, and analytical applications. UDM is mapped to a host of heterogeneous back-end data sources, providing a complete and integrated picture of your business, regardless of the location of your data.

Proactive Caching
Proactive caching combines real-time updates with OLAP class performance. SSAS maintains a highly compressed and optimized data cache that is automatically maintained as the data in the underlying source databases changes. The cache provides superb query performance and isolates the back-end source systems from the load of the analytical queries.

Enhanced Security Design
SSAS contains over 150 security design changes, including the following:
=> Out-of-the-box "secure by default" mode with multiple lines of defense.
=> Fine-grained administrative permissions.
=> Encrypted local cubes.
=> The lowest possible level of permissions.
=> Encrypted and signed client/server communications to help secure against packet sniffing, spoofing, tampering, and repudiation.