Intel has a long history using SAS and its visualization front-end JMP.
Both of those products now support augmentation with the open source R language.
So prototyping the analytics needed to support Hadoop cloud technology is probably underway using SAS/JMP and R. But there is much more open source software that emulates the MATLAB engineering math tools, which are probably a second part of the mix. I am sure that by now (Its already 11 days since this article was posted) there must be many more web postings on the big data analytics open source catalog of tools and even the likely need of GUI's to supported them in office world. So how about another update on that catalog and perhaps current global usage trends? Machine learning algorithms, for example, used by everyone from engineers to marketers and economists could be found in open source. Many big data blogs are hyping all of these open source tools. Somebody needs to mine those blogs, talk to Intel, and forecast some scenarios for "the rest of us." You might be just that forecaster.
What are the engineering and design challenges in creating successful IoT devices? These devices are usually small, resource-constrained electronics designed to sense, collect, send, and/or interpret data. Some of the devices need to be smart enough to act upon data in real time, 24/7. Are the design challenges the same as with embedded systems, but with a little developer- and IT-skills added in? What do engineers need to know? Rick Merritt talks with two experts about the tools and best options for designing IoT devices in 2016. Specifically the guests will discuss sensors, security, and lessons from IoT deployments.