Top Resources

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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The business case for earlier software defect detection and compliance

Regardless of the industry your business operates in, software is likely all around it. Software powers our cars, airplanes, and even the medical devices we rely on to diagnose and treat illness...

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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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Unpacking Open Source with OpenLogic

See how OpenLogic helps streamline open source scanning, reporting and governance, while keeping you on top of the latest security vulnerabilities.

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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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OpenLogic overview

Leverage the power of open source without adding risk to your development cycle times, adding unknown license obligations, or introducing security risks. Know, inventory, use, and resolve all aspects of open source software within your organization with this enterprise-class set of management and scanning tools coupled with the only guaranteed SLA first-line support for hundreds of open source software packages.

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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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HydraExpress overview

When performance and reliability are critical to application success, HydraExpress helps you rapidly respond to changing business needs and capitalize on your existing critical business applications in a SOA environment.

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Top 3 strategies to reduce risk in automotive / in-vehicle software development

Developing in-vehicle embedded applications is a safety, security, and quality challenge. Given that cars and trucks are increasingly connected to each other and to the devices around them, it’s becoming harder and harder to ensure that software is functional and free from risk. Development teams, especially the managers who are ultimately responsible, face incredible challenges when building such applications, and are learning that team members need to do more than just catch code defects during verification and validation testing. The new imperative: Identify and address security and compliance concerns earlier in the lifecycle, all while delivering innovative and differentiating features.

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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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Software as a process

Today’s software products are the result of many suppliers, vendors, open source repositories, and legacy code coming together in a mix of different processes, standards, and cultures. Each input offers a chance to introduce safety, security, or performance-related errors.

This paper explains the challenges of this polyglot environment and how strategies and tools proven in a number of industries can be applied to your organization to reduce defects, meet requirements, and minimize costs.

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