What’s Driving Big Data in the Enterprise – and Why Now?

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BigData Authors: William Schmarzo, Elizabeth White, Zakia Bouachraoui, Carmen Gonzalez, Andreas Grabner

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Population health management with RevoScaleR

This guest post is by Douglas McNair MD PhD, Engineering Fellow & President, Cerner Math Inc. -- ed. RevoScaleR scaling big-data modeling performance for real-time health data analysis at Cerner The size of data sets is increasing much more rapidly than the speed of cores, of RAM, and of disk drives. This is particularly true of electronic health records (EHRs) and other health care data, where multi-billion-row relational tables with dozens to thousands of columns are common in data warehouses derived from EHRs. Analyzing a data set that contains N observations on P variables frequently entails operating on several terabyte arrays — too large for main memory, beyond the statistical packages’ typical limit of a maximum vector length of 231 – 1 (around two billion cells), and too large even for pagefile virtual memory on a single machine. Statistical software able to use multi-core processors is a necessary, but not sufficient requirement to deal with data of this size. For statistical analysis of large data, you also need: parallelized external memory algorithms (PEMAs) that do not require all data to be in RAM, and efficient multi-threading and inter-process communication across massively parallel clusters of computers. Like other statistical packages, the R system is memory-limited. Revolution Analytics' RevoScaleR and bigmemory and a few other big-data packages overcome the memory limitation of R by providing a backing file for processing blocks of data at a time or by providing an ODBC or other streaming service for processing blocks of data. Since cardinality N is large, especially for our pharmacovigilance (drug safety) and other use-cases that involve modeling low-eventrate low-prevalence outcomes, only tasks with complexity that is approximately O(N) are feasible.  But not all big-memory and parallelized architectures offer comparable performance or scalability. For example, Lumley’s biganalytics package in R provides a method for passing big.matrix objects to the biglm function, which computes ordinary least squares regressions on large datasets, like one of Cerner’s cohorts of Type 2 diabetics: library(biglm)library(foreach)biglm.big.matrix( los.outlier ~ gender + age.grp + medicaid + last.HbA1c + max.creat + min.glu + nbr.adm24mo + nbr.comorbid_dx + nosocomial.inf, family=binomial(), data="type2_diabetic" ) And Revolution R Enterprise can do the same with RevoScaleR: rxLogit( los.outlier ~ gender + age.grp + medicaid + last.HbA1c +  max.creat + min.glu + nbr.adm24mo + nbr.comorbid_dx + nosocomial.inf,  data="type2_diabetic.xdf" ) Happily, the biglm regression takes less than 20 seconds to run on this cohort of 2 million diabetic patients and 10 variables.  But even more remarkably, the rxLogit regression takes less than 0.5 seconds to run on this dataset. (Both analyses ran on a Windows 7, 64-bit quadcore 2.5 GHz laptop with 8 MB of RAM.) And the RevoScaleR regression runs far faster than this on a multi-node cluster. The difference might seem inconsequential if our use-case involved a one-time execution of a regression at model-development-time. However, a majority of our run-time applications instead involve near-realtime kmeans ‘clustering’ or ‘classification’ or ‘prediction’ use-cases with high cardinality and dimensionality, where close matches to a particular patient and her attributes are sought within very large cohorts, and this task is repeated for successive different patients every second.  For high-value, high-performance applications like these, RevoScaleR and related tools are fast becoming an indispensable part of Cerner’s armamentarium for addressing many use-cases in health services and population health management. Douglas McNair, MD, PhD, is president of Cerner Math, Inc., and one of three Cerner Engineering Fellows and is responsible for innovations in decision support and very-large-scale datamining. McNair joined Cerner in 1986, first as VP of Cerner’s Knowledge Systems engineering department; then as VP of Regulatory Affairs; then as General Manager for Cerner’s Detroit and Kansas City branches. Subsequently, he was Chief Research Officer, responsible for Cerner’s clinical research operations. In 1987, McNair was co-inventor and co-developer of Discern Expert®, a decision-support engine that today is used in more than 2,000 health care facilities around the world. Between 1977 and 1986, McNair was a faculty member of Baylor College of Medicine in the Departments of Medicine and Pathology. He is a diplomate of the American Board of Pathology and the American Board of Internal Medicine.

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More Stories By David Smith

David Smith is Vice President of Marketing and Community at Revolution Analytics. He has a long history with the R and statistics communities. After graduating with a degree in Statistics from the University of Adelaide, South Australia, he spent four years researching statistical methodology at Lancaster University in the United Kingdom, where he also developed a number of packages for the S-PLUS statistical modeling environment. He continued his association with S-PLUS at Insightful (now TIBCO Spotfire) overseeing the product management of S-PLUS and other statistical and data mining products.<

David smith is the co-author (with Bill Venables) of the popular tutorial manual, An Introduction to R, and one of the originating developers of the ESS: Emacs Speaks Statistics project. Today, he leads marketing for REvolution R, supports R communities worldwide, and is responsible for the Revolutions blog. Prior to joining Revolution Analytics, he served as vice president of product management at Zynchros, Inc. Follow him on twitter at @RevoDavid