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Tech tutorial: Embedding analytics into a database using SourcePro and JMSL

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 JMSL Numerical Library, a native Java library from Rogue Wave Software. It describes in detail how to implement embedded JMSL using a particular relational database management system (RDBMS). As well as query the embedded JMSL in RDMBS 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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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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