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Tech tutorial: Embedding analytics into a database using SourcePro and IMSL .NET

There are numerous benefits to using embedded analytics including real-time analysis, faster results, better quality of data, and higher security. This white paper describes how to implement embedded analytics within a database using SourcePro and the IMSL .NET Numerical Library, a native C# library from Rogue Wave Software. It describes in detail how to implement a server-side native stored procedure leveraging IMSL .NET using a particular relational database management system (RDBMS), and execute the procedure in RDBMS using SourcePro DB.

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Prototype to production with IMSL Numerical Libraries

In the development of software that requires advanced math, statistics, or analytics, there is often a disconnect early in the development process. This occurs at the transition from algorithm selection and testing to the beginning of coding in the actual compiled language. We refer to this as the prototype to production transition.

To address the disconnect during prototype to production, we are presenting a method to run IMSL Numerical Libraries routines in R or Matlab. The goal is not to replace the algorithm developer’s tool of choice but to run a compiled version of the code in parallel. Pitfalls can be caught early, and data discrepancies can be resolved quickly by running the script version and compiled version side by side.
 

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See it for yourself: Analytics in IMSL C# vs. R

Statistical analysis and desktop modeling are often performed with software such as the well-known R Project yet sometimes a more robust and fully-featured framework is required. It's also common for statisticians and programmers to spend time re-implementing functions and not realize that they already exist elsewhere.

Using this MSDN article as a starting point, this paper offers up an alternative way of deploying statistical analysis: the IMSL.NET Numerical Library (C#). By walking through the examples presented in the article, you will see how the same numerical results are achieved in less time and with less complexity than trying to do it in R.

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Leveraging a .NET Numerical Library with Microsoft Excel

Extending the functionality found in Microsoft Excel. Specialized mathematical and statistical libraries can extend analysis techniques well beyond any spreadsheet tool. However, spreadsheets are very popular because of their ease of use in organizing data and obtaining results quickly. The combination of an advanced numerical library and a spreadsheet with extensibility features lets a developer use a common interface for powerful numerical analysis.

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Parallel performance of the IMSL C# Numerical Library

The IMSL C# Numerical Library uses the task parallel library (TPL) in .NET 4.0 to enable parallelism on shared memory systems, especially multicore systems. Starting with Version 6.5 of the IMSL C# Numerical Library, released in April 2010, codes using TPL were added to a variety of classes in the library. The goal for the release was to take advantage of multicore systems while minimizing impact on existing user code that references the IMSL C# Numerical Library and also minimizing the engineering resources in developing the release. Under these constraints, existing algorithms in the library were not re-written, but instead parallelized by adding TPL codes wherever sensible.

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IMSL C# Numerical Library for Microsoft .NET and Visual Studio solution brief

NextSigma develops, sells, and supports an integrated software solution that enables Six Sigma technologists to accomplish project management, process improvement and data analysis within the Define-Measure-Analyze-Improve-Control (DMAIC) and Define-Measure-Analyze-Design-Validate (DMADV) methodologies. Based on input from NextSigma’s customers, NextSigma was tasked to investigate how to provide advanced data analysis capabilities within their software solution that integrated with Microsoft® Visual Studio® and Microsoft® Visual Basic® development systems with minimum development, cost, and effort.

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High performance applications with advanced numerical analysis on the .NET framework

In recent years, the programming paradigm of the .NET framework has quickly been adopted for advanced numerical analysis with the financial services community, long known as early adopters leading the movement. The .NET framework is a strong platform for many reasons, including advanced programmer productivity, type safety, security policies, but with off-the-shelf convenience. However, many programmers may question the suitability of the platform for advanced numerical applications.

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NextSigma case study

NextSigma develops, sells, and supports an integrated software solution that enables Six Sigma technologists to accomplish project management, process improvement, and data analysis within the structured Define-Measure-Analyze-Improve-Control (DMAIC) and Define-Measure-Analyze-Design-Validate (DMADV) methodologies.

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