01. Jeff Veis, HPE Software, visits #theCUBE!. (00:18) 02. Reorganizations after the Micro Focus Deal. (01:18) 03. Micro Focus Affect on Deal Flow. (02:30) 04. Vertica: Continued Success and What's New. (04:42) 05. Analyzing in Place. (08:16) 06. Understanding the Crown Jewel that is IDOL. (09:54) 07. Data Sources for IDOL. (16:56) 08. More About IDOL and Comparing to Watson. (18:00) 09. Public Trials Available for the Newest IDOL. (20:11) Track List created with http://www.vinjavideo.com. --- --- HPE’s big data solutions add machine learning and natural language into the mix | #HPEDiscover by BEV TERRELL It’s been a very big year for big data, and Hewlett Packard Enterprise Co. is helping enterprises get a handle on organizing and analyzing their data with tools that analyze in place and offer machine learning and cognitive capabilities, according to Jeff Veis, VP of marketing, big data platform, at Hewlett Packard Enterprise Co. Veis spoke to Dave Vellante (@dvellante) and Paul Gillin (@pgillin), co-hosts of theCUBE*, from the SiliconANGLE Media team, during HPE Discover EU, held in London. (*Disclosure below) They discussed how HPE helps its customers get a grip on big data, as well as a fascinating new feature on the latest version of IDOL, a natural language question-answering function that is powered by machine learning. Veis also discussed HPE big data solutions, including Vertica, which enables users to conduct data analysis, regardless of where the data resides; IDOL, a software solution that provides a single environment for structured, semi-structured and unstructured data; and Haven, an on-demand platform of more than 60 advanced machine-learning APIs and services. A big focus on big data One of the concepts Veis talked about was “analyze in place,” or ways customers can get the value out of their datalake. With Vertica, they get the performance of a Vertica front end with the economy of Hadoop. Vertica’s new Frontloader was released in September; it added MS Azure support, in addition to AWS. So if companies want to ship most or all of their Vertica to the public cloud, they now have that option, Veis explained. Veis said that cloud gets interesting when a customer wants to bring in new workloads without affecting their current workload. With Vertica, they have degrees of freedom about where they can do that. For example, you can use Hadoop datalakes or spin-up instantiation in the cloud or go to another cloud. “That flexibility is when you can see the real value that hybrid compute brings,” said Veis. IDOL’s new language functionality The latest version of IDOL has a new feature called ‘natural language question answering’ — users can ask the computer questions, and it answers back in a humanistic way. Unlike the consumer products Siri and Amazon Echo, which use a verbal interface, IDOL creates an “answer server,” drawing from 500 different data sources and a thousand different file formats. As is the nature of machine learning, it will get smarter over time, using cognitive functions When you ask IDOL a question, it will decide based on your question and the data that it has which one of three engines to use. The first engine is called Answer Bank, which is able to parse the information in query (i.e., “Why is the sky blue?” and give you a prescribed answer in its data bank). The second engine is called the Fact Bank, and it can understand you’re looking for a fact, something that is not subject to interpretation (i.e., “What was the price of wheat trading at on October 5 at 4 PM?”). The third engine is called Passage Extract, where you can ask a general question (i.e., “What is the big topic of discussion in London today?”), and it extracts the gist of what the question is asking and, in this case, will scan newspaper headlines to give you an answer to that question. All three engines are based on high-performance, contextual analytics to be able to take that kind of request and produce an answer, Veis explained.