Big Data

HPE boosts IDOL data analytics engine with natural language processing tools

HPE recently announced that a new version of its analytics engine HPE IDOL will improve the interactions between humans and computers, adding context to data.

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Image: iStockphoto/Bet_Noire

HPE has added natural language processing capabilities to its HPE IDOL data analytics engine, which could improve how humans interact with computers and data, the company announced Tuesday. By using machine learning technology, HPE IDOL will be able to improve the context around data insights, the company said.

One of the biggest roadblocks to effectively leveraging big data is being able to have your questions answered simply, using language that feels normal, according to an HPE press release. Chatbots and other voice assistants have helped somewhat, the release said, but they aren't as feature-rich as some enterprises need.

So, the company has introduced HPE Natural Language Question Answering to its IDOL platform to help solve the problem. According to the release, the technology seeks to determine the original intent of the question and then "provides an answer or initiates an action drawing from an organization's own structured and unstructured data assets in addition to available public data sources to provide actionable, trusted answers and business critical responses."

SEE: Research: Companies lack skills to implement and support AI and machine learning (Tech Pro Research)

"IDOL Natural Language Question Answering is the industry's first comprehensive approach to delivering enterprise class answers," Sean Blanchflower, vice president of engineering for big data platforms at HPE, said in the release. "Designed to meet the demanding needs of data-driven enterprises, this new, language-independent capability can enhance applications with machine learning powered natural language exchange."

The natural language functionality will be core to the IDOL platform, and comes with the release of HPE IDOL 11.2, which is available now. Here are the features that will comprise its functionality:

1. IDOL Answer Bank - Gives set, curated answers to predetermined reference questions. One example cited by HPE in its release would be instructions for configuring and setting up a smartphone handset.

2. IDOL Fact Bank - Gives answers to specific questions based on facts. So, for example, a user could query the system for a specific stock price on a given day, and it would look through enterprise data to extract and return the answer.

3. IDOL Passage Extract - Gives a summary text overview for certain events, topics, or people. It uses freeform text data analysis to provide context.

4. IDOL Answer Server - Uses the previously mentioned features and analyzes questions and additional data to figure out the best way to answer a given question.

According to HPE's release, the tool is language independent and can integrate with other third-party systems like the NADIA language system.

"To be competitive, it is crucial for organizations to extract maximum value from their informational assets," SEC 1.01 AG CTO David Meyer said in the release. "HPE IDOL Natural Language Question Answering brings the power of Big Data to the fingertips of end users in a way that is tailored for humans."

Natural language processing is one of the core tenets of the modern AI movement, and HPE will be competing with a plethora of providers in the space. IBM's Watson is known for its language capabilities, and some cloud providers have even stepped into the space, with Amazon announcing its Polly service at AWS re:Invent in November.

The 3 big takeaways for TechRepublic readers

  1. HPE is introducing a new features called Natural Language Question Answering to HPE IDOL 11.2.
  2. Natural Language Question Answering utilizes four different key functions ranging from fact-based answering to extraction of text passages.
  3. HPE will need to compete with other enterprise services like Amazon's Polly and IBM Watson to gain market share for IDOL.

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About Conner Forrest

Conner Forrest is a Senior Editor for TechRepublic. He covers enterprise technology and is interested in the convergence of tech and culture.

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