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robably the simplest business intelligence (BI) tools an organization can take advantage of are reports created with SQL Server Reporting Services (SSRS) However, end users have limited dynamic capabilities when they view a report You can extend the capabilities of SSRS with the help of report models, which are covered briefly in 11, Extending and Deploying SSRS Reports But using report models to build reports is an advanced skill for end users, and the reports performance is limited In addition, classic reports do not show analysis over time You can eliminate both the need for SSRS advanced skills and problems with limited performance by using SQL Server Analysis Services (SSAS) to implement data warehouses and online analytical processing (OLAP) systems.

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Aspose.Pdf.Genertor is obsolete but Aspose.Pdf namespace is new Document Object Model which provides the capabilities to create as well ...

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Performance Console Differences. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12-7

Monitoring Network Traffic with Netstat . . . . . . . . . . . . . . . . . . . . . . . . . . 12-13

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NET PDF Viewer component. With Spire.PDFViewer, developers can create any WinForms application to open, view and print PDF document in C# and Visual ...

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End users can get information for analysis at lightning speed, and they can change the view of information in real time, drilling down to see more details or up to see summary information However, end users are still limited in a few ways with these systems Typically, users do not have time to examine all possible graphs and PivotTable dynamic views using all possible attributes and hierarchies In addition, they can search only for patterns they anticipate, and OLAP analysis is typically limited to basic mathematical operations, such as comparing sums across different groups SSAS data mining addresses most of these limitations Data mining is data-driven analysis With data mining, you as the analyst do not anticipate patterns in advance You examine data through advanced mathematical methods instead, and then you examine patterns and rules that your algorithm finds.

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Because the new application will only need to access data from a SQL Server database and you are targeting the .NET Framework, you can use the ADO.NET data provider. It is unlikely you will need to take advantage of the newer features available with SQL Server, given the application is being converted from an older application and given your time constraints. For that reason, it is not necessary to consider SNAC as a data provider. You can also use TCP/IP as the network protocol to provide the fastest access to the data across the Internet. You can also suggest that they use Visual Basic .NET as the language because the development team is already accustomed to using prior versions of Visual Basic. This should help to speed the conversion time.

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Because the mining algorithms work automatically, they can check millions of different pivoting options in a very limited time In this chapter, you will learn how you can use SSAS data mining to give users the BI capabilities they need to make better, more informed decisions First you will learn how to develop several SSAS data mining models and select from those the best models for deployment into production You will learn how to assess the accuracy and reliability of the models and compare the performance of the models so that you can choose the best model for your business needs Then you will see how to secure the models and how to create prediction queries In addition, you will learn how to deploy a mining model by using it in an SSRS report..

In cases where you are not able to modify Transact-SQL directly, you can still improve query performance using a plan guide. The plan guide is used to override the execution plan automatically generated by the query optimizer. As long as the execution plan you come up with returns the same results and performs better than the original query, this is a good way to affect performance without altering the code.

Lesson Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12-19

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