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CAS complex simulations case study

Wyle’s CAS Group reduces development cycles, costs, and experiences significant performance gains...

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Software supply chain series part 1: Three ways to secure your software supply chain

Organizations are really concerned with cyber-attacks, but not all organizations know the best way to address the issue...

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Reduce your open source security risk: Strategies, tactics, and tools

Open source software is here to stay but that doesn't mean that developers can use it without considering the vulnerabilities and security issues they may introduce into their development projects.

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3 part series by Forrester Research: How businesses can succeed in the age of the customer

In this three part series, Kurt Bittner, principal analyst with Forrester Research talks about how businesses can succeed in the age of the customer, and why developers matter now, more than ever.

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Lessons learned: Using a static analysis tool within a continuous integration system

Static analysis tools are used for improving software quality and reliability. Since these tools can be time consuming when used for analysis of big codebases, they are normally run during scheduled (e.g. nightly) builds. However, the sooner a defect is found, the easier it is to fix efficiently.

In order to detect defects faster, some analysis tools offer an integration with the integrated development environment of the developers at the cost of not always detecting all the issues. To detect defects earlier and still provide a reliable solution, one could think of running an analysis tool at every build of a continuous integration system.

In this IEEE paper, we share the lessons learned during the integration of the static analysis tool Klocwork (that we are developing) with our continuous integration system. We think that the lessons learned will be beneficial for most companies developing safety-critical software (or less critical systems) that wish to run their analysis tool more often in their build system. We report these lessons learned along with examples of our successes and failures.

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Use support vector machines in JMSL

Support vector machines were implemented as part of the latest release of JMSL, along with expanded data mining functionality, including areas such as decision trees and bootstrap aggregation. The documentation of the IMSL Libraries is detailed and robust, but the algorithm discussion and examples can only cover a finite set of use cases.

This white paper will walk you through examples not covered in the documentation, with a focus on classification. We start with the textbook examples that are part of most SVM resources, providing notes and key points throughout.

Download this complimentary resource now, and receive access to a full code example which follows the examples outlined.

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Visualization Application Review

The Visualization Application Review is ideal for companies with highly-customized graphical user interfaces. During this review, Rogue Wave Software professional services consultants conduct an unbiased, expert usability and performance assessment of your applications that rely on Rogue Wave Visualization products: Views (C++), JViews (Java), or Elixir (FLEX).

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Using IMSL C on relational data with SourcePro DB

SourcePro DB supports a wide variety of databases and can be used to retrieve, manipulate, and analyze data, including very large data, using the statistical analysis functions provided by the IMSL C Numerical Library.

This paper provides a code example that uses SourcePro DB to harvest data from a database, analyzes that data using IMSL C, and then updates the database with the processed data.

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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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Overcoming relational database limitations with NoSQL

​Relational databases are the workhorses of the modern database industry. They have limitations, however, when it comes to handling some types of data, in particular the large quantities of free-form data generated through mobile technology. NoSQL databases provide solutions for some of these problems but they introduce another problem in having no single query language that drives them.

This paper examines the problem, surveys the solutions, and answers the question of how to implement the solutions through a consistent API.

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