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

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 C 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 C using a particular relational database management system (RDBMS). We then execute the procedure in RDBMS using SourcePro DB.

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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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Using JMSL in Apache Spark

Data in all domains is getting bigger — and because more data presumably equates to more accuracy in measurement, “big data” is generating the promise of new insights and understanding.However, the technical challenges posed by big data can overwhelm engineering teams who are
seeking ways to optimize their work effort.

This technical white paper illustrates two examples on how to use JMSL classes on Spark resilient distributed datasets (RDDs) to leverage the advanced mathematics and statistics algorithms in JMSL in distributed Spark applications written in Java.

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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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Tech tutorial: Embedding analytics into a database using JMSL

Responding to the many challenges of Big Data means coming up with specific tactics and tools for specific problems, such as using Hadoop and the MapReduce framework for storing and processing very large data sets. Hadoop embodies one of the fundamental changes in Big Data, bringing the algorithms to the data instead of separating the analysis from the storage.

One specific tactic to bring the algorithms to the data is to embed analytics into the database. This paper presents a walkthrough, with code samples, of how to embed JMSL predictive analytics into an Oracle 12c database, using the Naive Bayes classifier algorithm and an example test data set. The complete Java code for this exercise is presented in the appendix.

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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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Using JMSL in Hadoop MapReduce applications

The excitement around big data is the expectation of better results and new insights as we take more accurate measurements of our world. Among the many important considerations to help reach this potential is deciding how to perform efficient mathematical and statistical analysis on the data when traditional storage methods are no longer feasible, reasonable, or possible. The answer is to combine Hadoop MapReduce with JMSL Numerical Libraries.

Learn more by walking through a few technical examples and code of how to use JMSL Numerical Libraries in Hadoop MapReduce applications.

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Rogue Wave tools and libraries for big data

Big data applications are among the fastest-growing and demanding in the business and research communities, encompassing a range of workloads in real-time analytics, data mining, complex event processing, MapReduce applications, and visualization. Besides the need for high performance, big data applications must also handle data complexity, varying data properties, and optimizing storage capacity.

In this report, senior analyst Michael Feldman from Intersect360 Research discusses these challenges in the big data developer market and presents an analysis of how unique tools and libraries from Rogue Wave Software increase developer productivity and reduce deployment and maintenance costs.

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Rogue Wave tools and libraries for financial services

Financial services is the second largest vertical market of high performance computing, including high-frequency trading, risk management, securities and derivatives pricing, and economic analytics. With increased competition, a growing skills gap, and stronger regulations, it's more challenging than ever to deliver applications that meet demanding performance requirements.

In this report, senior analyst Michael Feldman from Intersect360 Research discusses the current financial services developer market and presents an analysis of how unique tools and libraries from Rogue Wave Software increase developer productivity and reduce deployment and maintenance costs.

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IMSL Numerical Libraries

Analyzing data has never been more important — or harder. Getting actionable results from your large and very large datasets can often determine if your organization meets its goals. Create competitive differentiation and unlock innovation by using the most trusted, tested, and reliable algorithms available. Backed by a team of mathematicians and statisticians, IMSL® Numerical Libraries allow you to address complex problems quickly by using the right algorithm. With IMSL you get consistency from prototype to production.

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