SYSTEMS

LexisNexis Risk & Information Analytics Group today announced that Sandia National Laboratories is leveraging the company’s Data Analytics Supercomputer (DAS) to address the challenges posed by exponential growth of data sets and next-generation informatics applications. In competitive performance tests conducted by LexisNexis Risk & Information Analytics Group and Sandia, results show that the DAS, which is a leading-edge, high performance computing system (HPCC), performed 10 times faster than the next fastest system for large data analysis.

“While open source systems like Hadoop have come a long way in solving next-generation data challenges, the LexisNexis Data Analytics Supercomputer takes data analytics to the next level and enables extreme high-performance computing at a scale not previously available,” said Armando Escalante, Chief Technology Officer of LexisNexis Risk & Information Analytics Group.

For more than thirty years, LexisNexis has been on the frontier of large-scale data management and analysis. The DAS was originally developed by LexisNexis Risk & Information Analytics Group to solve its internal data management and large-scale data analytics challenges and to deliver the speed and accuracy demanded by its expanding customer base. Today, the DAS powers all of the company’s risk management solutions and helps customers solve large, complex data challenges such as national security issues.

Designed to manage the most complex and data-intensive analytical problems, the DAS features industry-leading speed, capacity, accuracy and ease of use. Tpowered by high performance computing cluster (HPCC) technology, the DAS was designed to be able to process, analyze, and find links and associations in high volumes of complex data significantly faster and more accurately than current technology systems. In addition, the DAS scales linearly from 10’s to 1000’s of nodes handling 10’s of petabytes, supporting millions of transactions per day.

The core of the DAS is the Enterprise Control Language (ECL), which is a declarative, non-procedural programming language optimized for large-scale data management and query processing, which automatically manages workload distribution across all nodes. This benefits programmers, who do not need to understand how to manage the parallel processing environment. ECL programming efficiency is proven to be far greater than other approaches.

Sandia National Laboratories is currently using the DAS to determine its potential for integration into a system of large-scale informatics computing platforms. In one configuration, the DAS manages the massively large data volumes generated by collision simulations and other data-intensive applications already used by the labs.

“Traditional supercomputing technology allows us to run complex physics applications and visualize detailed simulations,” said Dr. Richard Murphy, senior member of technical staff at Sandia National Laboratories. “However, these systems are not ideal for the informatics challenge of sorting through petabytes of data to find correlations and generate hypotheses. Our tests show that the DAS is a strong platform for helping us address these challenges.”

The DAS looks for specific patterns and non-obvious relationships to better identify possible outcomes from the Sandia simulations. By accurately managing the massive data sets generated in these operations, the DAS enables traditional systems to more quickly extract relevant data to process scientific calculations in their core memory at high-speed.

“We are pleased to partner with Sandia National Labs in finding new ways of applying our technology to solve complex informatics challenges,” said Haywood Talcove, chief executive officer, LexisNexis Special Services Inc., a trusted leader in enabling government agencies to transform data into mission critical decisions. “In tests conducted with Sandia National Labs, the DAS was shown to process complex algorithms faster and more efficiently than current marketplace systems.

In a second application, Sandia is working with LexisNexis Risk & Information Analytics Group to assess the applicability of the DAS to solve problems associated with natural language processing and machine comprehension of unstructured text.