Dell, EMC, Dell Technologies, Cisco,

Showing posts with label MLib. Show all posts
Showing posts with label MLib. Show all posts

Sunday, July 16, 2017

OpenText vows to take on IBM Watson with new Magellan AI platform

IEnterprise information management company #OpenText has announced the general availability of its new #artificialintelligence ( #AI ) and cognitive computing platform, #Magellan, at its Enterprise World conference in Toronto. Attendees at the show were left in little doubt as to how senior executives at the Waterloo, Ontario-based company would like them to view Magellan: as a direct competitor to #IBM ’s #Watson. OpenText isn’t targeting Salesforce’s Einstein, nor similar AI-based initiatives from #SAP or #Pegasystems, CEO Mark Barrenechea stressed in a press conference at the event: It’s Watson [that we’re targeting]. What differentiates us is that the information-centric applications that we already have will be integrated directly with Magellan. We don’t want to extract information – from contracts, for example – out of applications and into an AI platform. The fact that you have to feed Watson with data extracted from multiple resources, I think, is a weakness. In other words, OpenText is aiming to embed Magellan’s AI capabilities in some of the tools that comprise its vast portfolio of software products. These typically focus on unstructured and semi-structured information, in areas such as enterprise content management, customer experience and data discovery. Where the company believes Magellan can stand out is in its understanding of human language (as opposed to transactional data), as it is expressed in documents such as reports, memos, emails, contracts and requests for information, via text mining and semantic analysis. Built by OpenText? This looks likely to be a significant challenge, because OpenText has largely been built through acquisition. In the last 20 or so years, it’s acquired over 50 different information management companies. Most recently, these have included its $1.62 billion purchase of #DellEMC s enterprise content software, which included former arch-rival #Documentum, in a deal that closed in January, and its April acquisition of customer experience software and service assets from #HPE, for $170 million. In fact, #Magellan itself has its roots in technology acquired in two acquisitions: the 2010 purchase of #NsteinTechnologies, which brought text mining and auto classification capabilities to OpenText; and the 2015 purchase of Actuate, which brought it analytics capabilities. On top of these technologies, OpenText has introduced integration with two open source products: big data framework #ApacheSpark and #ApacheMLlib, a machine learning library. Algorithms for Magellan can be programmed in languages including #Scala, #Python and R. But as Barrenechea stressed: There’s also a whole ton of our own engineering in there. We may start with M&A, but if we can’t grow a product, if we can’t innovate, if we can’t get it to evolve, then we’re not interested in buying it. Magellan is built by OpenText. Challenges ahead It’s no surprise that Barrenechea is keen to stress OpenText’s engineering and innovation credentials. With the launch of Magellan and the shift into AI, the company will find itself in a fiercely competitive and fast-moving field of technology. It could prove to be an unforgiving environment for a company built on acquisition, as opposed to organic growth fuelled by new products. As Barrenechea told analysts on the company’s third quarter results call: M&A remains our leading growth driver, and we will continue to grow and invest in our M&A team. It’s hard, then, not to draw parallels with the kind of strategy made famous by CA Technologies during the 1990s and early 2000s: buy up intellectual property from other software companies, provide basic maintenance and milk the installed base. The fact that Barrenechea worked at CA Technologies (then known as Computer Associates) in various roles during those years – as product development head, president of technology strategy and chief technology officer – simply reinforces that comparison. To sweeten the deal for prospective Magellan customers, the product will be sold at a much lower price point than IBM Watson, will be available as an appliance that runs on preconfigured, commodity hardware and be available on premises or via the cloud. A lower cost of entry could appeal to a wider range of customers than might consider IBM Watson a viable investment. As Barrenechea put it: With OpenText Magellan, we are moving towards a more open, scalable and affordable future for AI. OpenText is committed to ensuring AI is designed into its offerings from day one.

http://diginomica.com/2017/07/14/opentext-vows-take-ibm-watson-new-magellan-ai-platform/

Thursday, June 2, 2016

Big Data Benchmark Gauges Hadoop Platforms

In another indication of a maturing technology and growing demand, an industry group has released a big data analytics benchmark designed to gauge the performance of #Hadoop -based systems.

The Transaction Processing Performance Council said this week its TPCx-BB benchmark for big data analytics systems covers systems such as #MapReduce, #Apache #Hive, Apache #Spark and Machine Learning Library, or #MLib.

According to the TPC website, the “express” benchmark measures the performance of Hadoop-based systems, including hardware and software components. The benchmark executes 30 frequently performed analytical queries in the context of “retailers with physical and online store presence.”

The queries are expressed in SQL for structured data and in machine learning algorithms for semi-structured and unstructured data. SQL queries can use Hive or Spark while machine learning algorithms use MLib along with “user defined functions and a procedural program,” the benchmark group added.

Along with representing the three data types, the new benchmark simulates big data processing, analytics and reporting for the 30 use cases. Runtimes for the big data simulations range from seconds to hours.

The benchmark workload also addresses data set scaling and can run concurrent threads supporting multiple jobs with different characteristics running on a single cluster or via node scaling. The metric supports Hive on MapReduce as well as Hive running on both Spark and Apache Tez, the framework for building high-performance batch and interactive data processing applications.

The benchmark characteristics ultimately provide performance and price metrics for determining the tradeoffs between data analytics performance and cost, the council said.

http://www.datanami.com/2016/06/01/big-data-benchmark-gauges-hadoop-platforms/