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AnalysisServices

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The document summarizes Microsoft's SQL Server 2005 Analysis Services (SSAS). It provides an overview of SSAS capabilities such as data mining algorithms, unified dimensional modeling, scalability features, and integrated manageability with SQL Server. It also describes demos of the OLAP and data mining capabilities and how SSAS can be deployed and managed for scalability, availability, and serviceability.

Technology
1 of 19
Deliver Rich Analytics with Analysis Services SQL Server Donald Farmer Group Program Manager Microsoft Corporation
Agenda ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Analysis Services Why OLAP and Data Mining Matter ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
What Is SQL Server 2005 Analysis Services? Dashboards Rich Reports BI Front Ends Spreadsheets Ad Hoc Reports SQL Server Teradata Oracle DB2 LOB DW Datamart Analysis Services ,[object Object],[object Object],[object Object]
Analysis Services High-level Architecture Dashboards Rich Reports BI Front Ends Spreadsheets Ad Hoc Reports Analysis Services Cache SQL Server Teradata Oracle DB2 LOB DW Datamart XML/A or ODBO UDM
Analysis Services High-level Architecture SQL Server Teradata Oracle DB2 LOB DW Datamart Analysis Services Cache UDM XML/A or ODBO
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],SQL Server Analysis Services New Paradigm for the Analytics Platform
The Unified Dimensional Model
Value of Data Mining ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],SQL Server 2005 OLAP Reports (Ad Hoc) Reports (Static) Data Mining Business Knowledge Easy Difficult Usability Relative New Business Insight
Complete Set of Algorithms Introduced in SQL Server 2000 Linear Regression Text Mining Decision Trees Clustering Time Series Sequence Clustering Association Naïve Bayes Neural Net Logistic Regression
Putting Data Mining to Work Clustering Sequence Clustering Finding groups of similar items. For example, to segment demographic data into groups to better understand the relationships between attributes. Association Rules Decision Trees Finding groups of common items in transactions. For example, to use market basket analysis to suggest additional products to a customer for purchase. Sequence Clustering Predicting a sequence. For example, to perform a clickstream analysis of a company's Web site. Decision Trees Time Series Predicting a continuous attribute. For example, to forecast next year's sales. Decision Trees Naive Bayes Clustering Neural Network Logistic Regression Linear Regression Predicting a discrete attribute. For example, to predict whether the recipient of a targeted mailing campaign will buy a product. Microsoft Algorithms Task
Mining for Meaning
BI App lifecycle Dev Server Test Server BI Dev Studio Management Studio Dev/Test ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Design Develop Debug Build Deploy Version Production Prod Server Prod Server Prod Server
Scalability ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Availability ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Serviceability ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Manageability ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
© 2005 Microsoft Corporation. All rights reserved. This presentation is for informational purposes only. Microsoft makes no warranties, express or implied, in this summary.

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AnalysisServices

  • 1. Deliver Rich Analytics with Analysis Services SQL Server Donald Farmer Group Program Manager Microsoft Corporation
  • 2.
  • 3.
  • 4.
  • 5. Analysis Services High-level Architecture Dashboards Rich Reports BI Front Ends Spreadsheets Ad Hoc Reports Analysis Services Cache SQL Server Teradata Oracle DB2 LOB DW Datamart XML/A or ODBO UDM
  • 6. Analysis Services High-level Architecture SQL Server Teradata Oracle DB2 LOB DW Datamart Analysis Services Cache UDM XML/A or ODBO
  • 7.
  • 9.
  • 10. Complete Set of Algorithms Introduced in SQL Server 2000 Linear Regression Text Mining Decision Trees Clustering Time Series Sequence Clustering Association Naïve Bayes Neural Net Logistic Regression
  • 11. Putting Data Mining to Work Clustering Sequence Clustering Finding groups of similar items. For example, to segment demographic data into groups to better understand the relationships between attributes. Association Rules Decision Trees Finding groups of common items in transactions. For example, to use market basket analysis to suggest additional products to a customer for purchase. Sequence Clustering Predicting a sequence. For example, to perform a clickstream analysis of a company's Web site. Decision Trees Time Series Predicting a continuous attribute. For example, to forecast next year's sales. Decision Trees Naive Bayes Clustering Neural Network Logistic Regression Linear Regression Predicting a discrete attribute. For example, to predict whether the recipient of a targeted mailing campaign will buy a product. Microsoft Algorithms Task
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  • 19. © 2005 Microsoft Corporation. All rights reserved. This presentation is for informational purposes only. Microsoft makes no warranties, express or implied, in this summary.
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