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HPCC Systems Flavio Villanustre VP, Products and Infrastructure HPCC Systems

HPCC Systems Flavio Villanustre VP, Products and Infrastructure HPCC Systems. Risk Solutions . INTRODUCTION. LexisNexis Risk Solutions More than 15 years of Big Data experience Provides information solutions to enterprise customers Generates about $1.4 billion in revenue

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HPCC Systems Flavio Villanustre VP, Products and Infrastructure HPCC Systems

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  1. HPCC Systems Flavio Villanustre VP, Products and Infrastructure HPCC Systems Risk Solutions

  2. INTRODUCTION LexisNexis Risk Solutions • More than 15 years of Big Data experience • Provides information solutions to enterprise customers • Generates about $1.4 billion in revenue • Has been using the HPCC Systems platform for over 10 years • HPCC Systems • Launched in June 2011 • Open source, and enterprise-proven distributed Big Data analytics platform • To help enterprises manage Big Data at every step in the Complete Big Data Value Chain Strata 2012 Keynote 2

  3. THE COMPLETE BIG DATA VALUE CHAIN Collection – Structured, unstructured and semi-structured data from multiple sources Ingestion – loading vast amounts of data onto a single data store Discovery & Cleansing – understanding format and content; clean upand formatting Integration – linking, entity extraction, entity resolution, indexing and data fusion Analysis – Intelligence, statistics, predictive and text analytics, machine learning Delivery – querying, visualization, real time delivery on enterprise-class availability Collection Ingestion Discovery & Cleansing Integration Analysis Delivery Strata 2012 Keynote 3

  4. MACHINE LEARNING IN BIG DATA • How do you extract value from big data? • You surely can’t glance over every record; • And it may not even have records… • What if you wanted to learn from it? • Understand trends • Classify into categories • Detect similarities • Predict the future based on the past… (No, not like Nostradamus!) • Machine learning is quickly establishing as an emerging discipline. • But there are challenges with ML in big data: • Thousands of features • Billions of records • The largest machine that you can get, may not be large enough… • Get the picture? Strata 2012 Keynote

  5. ECL-ML: HPCC SYSTEMS MACHINE LEARNING • A fully distributed and extensible set of Machine Learning techniques for Big Data • State of the art algorithms in each of the Machine Learning domains, including supervised and unsupervised learning: • Correlation • Classifiers • Clustering • Statistics • Document manipulation • N-gram extraction • Histogram computation • Natural Language Processing • Distributed and parallel underlying linear algebra library Strata 2012 Keynote

  6. TAKE AWAYS • A fully parallel set of Machine Learning algorithms on Big Data gives you full insight • Outliers matter, especially when those outliers are the exact reason for the discovery effort (for example, in anomaly detection) • Dimensionality reduction can conduce to information loss: why risk losing valuable information when you can have it all? • Leveraging a fully parallel machine learning solution on Big Data will help you identify fraud, bring products to market faster, and become more competitive • Organizations that don’t leverage the big data that they have, risk losing ground to their competitors • Get on it, now! Strata 2012 Keynote

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