SOURCE: LexisNexis HPCC Systems

LexisNexis HPCC Systems

November 13, 2012 12:24 ET

Opera Solutions Forms Strategic Alliance With LexisNexis® to Leverage HPCC Systems® Big Data Platform

ATLANTA, GA--(Marketwire - Nov 13, 2012) - LexisNexis Risk® Solutions today announced that Opera Solutions, LLC, a leading global Big Data science company, has chosen HPCC Systems®, an open-source, enterprise-proven platform for Big Data analysis and processing, as one of the processing components in its Vektor™ Big Data platform. Opera Solutions, whose global customers are in data-rich industries, will leverage the HPCC Systems' platform to rapidly process and analyze large internal and external data.

"Our mission is to very rapidly extract predictive Signals from Big Data flows and turn them into directed actions that help drive frontline productivity and bottom-line growth," said Arnab Gupta, CEO, Opera Solutions. "HPCC Systems' platform supports this mission through cutting-edge, proven technology that we can use to rapidly integrate and find Signals in large data flows."

HPCC Systems grew out of the need for LexisNexis Risk Solutions to manage, sort, link, integrate, and analyze billions of records within seconds. Opera Solutions will leverage HPCC Systems' consistent architecture, high-level data-centric programming language, and two processing platforms: the Thor Data Refinery Cluster and the Roxie Rapid Data Delivery Cluster. The HPCC Systems Big Data Platform will integrate with Opera Solutions' Vektor Big Data analytics platform, which supports the deployment of Opera Solutions' hosted analytics solutions.

"HPCC Systems continues to gain momentum and is poised to help Opera Solutions rapidly deliver value to its customers," said Flavio Villanustre, Vice President, Infrastructure, HPCC Systems. "Opera Solutions is a great example of a company that got involved in our open-source community in its endeavors in the Big Data analytics world, and the power of our platform is helping them continue to innovate."

About Opera Solutions, LLC
Opera Solutions (www.operasolutions.com) is a global leader in machine learning. With 230+ machine-learning scientists among its 700 employees, it combines science, technology, and domain expertise to transform Signals -- the valuable predictive information in Big Data flows -- into machine-generated Best Actions that drive profit and productivity gains. Its Signal Hub™ technologies provide an enterprise-wide capability to continually extract and use newly available predictive information from inside and outside the enterprise. Opera Solutions serves financial services, healthcare, government, supply chain, marketing, and other sectors, with offices in North America, Europe, India, and China. For more information, contact Interest@operasolutions.com, or call 1-855-OPERA-22.

About HPCC Systems®
HPCC Systems® (www.hpccsystems.com) from LexisNexis® Risk Solutions offers a proven, data-intensive supercomputing platform designed for the enterprise to process and solve Big Data analytical problems. As an alternative to legacy technology, HPCC Systems offers a consistent data-centric programming language, two processing platforms and a single, complete end-to-end architecture for efficient processing. Connect with us on Twitter (@hpccsystems), Facebook (https://www.facebook.com/hpccsystems) and LinkedIn (http://www.linkedin.com/company/hpcc-systems)

About LexisNexis Risk Solutions
LexisNexis® Risk Solutions (www.lexisnexis.com/risk/) is a leader in providing essential information that helps customers across all industries and government predict, assess and manage risk. Combining cutting-edge technology, unique data and advanced scoring analytics, we provide products and services that address evolving client needs in the risk sector while upholding the highest standards of security and privacy. LexisNexis Risk Solutions is part of Reed Elsevier, a leading publisher and information provider that serves customers in more than 100 countries with more than 30,000 employees worldwide.

Contact Information

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