Top White Papers

A fast, scalable solution for solving the transportation problem

While the family of transportation problems can be solved by hand, at least for relatively small problems, the IMSL Library includes an algorithm that is fast and scalable.

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7 questions to select, deploy, and maintain open source software effectively

A question that often comes up in enterprise software organizations is: What happens when open source software doesn’t work?

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Prioritize defects faster with Klocwork SmartRank

The faster a bug is identified in code, the easier it is to fix. Klocwork SmartRank helps identify which issues to fix first.

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Enterprise API management defined

Defining API management and the best path to quickly capitalize on new revenue channels without assuming risk along the way.

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White Papers

2016 Open Source Support Report

Open source software (OSS) is the dominant force in software development today, driving technical innovation, enabling productivity gains, and touching everything from big data and cloud to mobile and embedded. Despite its importance and reach, there’s little infrastructure and even less knowledge on how to effectively support OSS.

This report takes real support data from the top OSS packages across different organizations to analyze the statistics, realities, and results of open source in the field.

For more recent data download the 2017 Open Source Support Report.

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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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Deterministically troubleshooting network distributed applications

Debugging is all about understanding what your software is really doing. Computers are unforgiving readers; they never pay attention to what you mean, and always insist on doing what the code says.

Debugging happens naturally when actively developing code and troubleshooting a problem. The same kind of investigation is also a great way to learn about programs that are working just fine. It pays to look closely at what programs are really doing when you re-introduce yourself to code that you wrote a long time ago, or when you try to understand a new bit of code that you encounter for the first time.

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Car cybersecurity: What do the automakers really think?

The Ponemon Institute recently conducted a cybersecurity survey sponsored by Rogue Wave Software and Security Innovation of over 500 automotive developers, engineers, and executives, primarily from automotive OEMs and tier one suppliers.

This paper analyzes the survey results, and provided new insights to help automotive software suppliers understand the current mindset of their developers and build security and safety into their software.
 

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Increase system availability by leveraging Apache Tomcat clustering

Open source is the dominant force in software development today, with over 80 percent of developers now using open source in their software. With an increase in use of open source software (OSS), support and knowledge of these packages becomes critical to companies. Apache Tomcat is one of the most popular open source software implementations in the industry, and one of the most requested support packages to Rogue Wave OSS support experts. Support requests related to Apache Tomcat cover many aspects of the software, but a popular topic is always high-reliability and clustering.

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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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