Pervasive

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• Fully exploit your servers to meet analytic challenges on growing data sets

  We’ll show you how on Dec. 8 Like most software organizations yours probably needs a cost-effective approach to deliver analytics or other data-intensive solutions amid increasing data volumes and growing processing complexity—one that allows you to enhance... Posted Dec 06 2010, 01:04 PM by Richard Maddox with | with no comments Filed under: DataRush, Pervasive DataRush, Jim Falgout, multicore revolution, Multicore, AMD, parallel processing, density-based clustering, clustering, dataflow, Pervasive, DataRush engine, parallelism, intel, parallelizing, runtime scalability, DataRush applications, scalable, parallelization, multicore processors, data-intensive applications, Pervasive Software, datarush-analytics, big data, parallel, parallel programming, multicore challenges, multicore hardware, Scalability, data-intensive, MalStone B, Hadoop, Analytics, density based clustering, distributed clustering, valuable information, dense computing, clusters

• Distributed, Scalable Clustering for Detecting Halos in Terascale Astronomical Datasets.

  The process of stellar discovery has long made its home at High Performance Computing (HPC) systems. HPC systems have evolved into clusters of "fat" multicore nodes. Applications must take advantage of parallelism across nodes and at the node... Posted Jul 02 2010, 09:47 AM by n5712036 with | with 1 comment(s) Filed under: DataRush, HPC, dataflow, Nena Marin, terascale, density based clustering, astronomical dataset, distributed clustering, stellar discovery

• Fraud Detection and “Finding a needle in a haystack”

  Fraud Detection and “Finding a needle in a haystack” Benford ’s law has been promoted as providing auditors with an automated tool that is simple and effective for fraud detection . The law of anomalous numbers was published in 1938 by Frank Benford ... Posted Mar 20 2010, 05:54 PM by n5712036 with | with no comments Filed under: DataRush, Predictive Analytics, parallel processing, accuracy, data mining, dataflow, UT, java, parallel data mining, Pervasive, parallelization, data-intensive applications, Predictive Analytics World, big data, Fraud, fraud waste abuse, NHIN, Benfords, healthcare fraud, Nena Marin, health connect, waste, abuse, healhit, benford, meaningful use, stimulus

• What would I do with 48 cores?

  I mentioned in an earlier blog, the contest sponsored by AMD around the release of their 12-core 6100 series processors. With a 4p system that gives you 48 cores on a single box! This is an amazing amount of compute power in a very compact form. This... Posted Mar 15 2010, 11:34 AM by jfalgout with | with 6 comment(s) Filed under: DataRush, Multicore, AMD, dataflow, java, data-intensive applications, algorithms, big data, 12 cores, parallel, terabyte an hour, terabyte, cyber security

• HIMSS 2010 - DataRush in Health IT making a difference in patient care

  The DataRush team just returned from attending the HIMSS 2010 conference in Atlanta (March 1-4 th ). HIMSS is traditionally a large conference. As massive as the Atlanta convention center is, this year once again HIMSS filled the venues. Total attendance... Posted Mar 11 2010, 08:13 AM by n5712036 with | with no comments Filed under: DataRush, Data Matching, Predictive Analytics, multicore revolution, Multicore, Datarush team, accurate, accuracy, data mining, dataflow, java, Pervasive, java applications, building block of predictive analytics, DataRush engine, dataflow model, bottleneck, predictive health application, DataRush library, DataRush applications, scalable, data-intensive applications, Pervasive Software, dataflow MPI map/reduce teraflops terabytes, java virtual machine, Java performance, algorithms, datarush-analytics, data parallelism

• Presentations Show Strength and Speed of DataRush Performance

  It has been a busy few weeks for Pervasive DataRush. First, we headed to Predictive Analytics World in Washington D.C. where our collaborative work with the University of Texas at Austin on predicting customer behavior for marketing and sales optimization... Posted Nov 10 2009, 04:38 PM by livey with | with no comments Filed under: dataflow, churn prediction, runtime scalability, predictive health application, WellMax Center

• Text Mining to extract content from Netflix Prize Movie Titles

  We recently published a recommender system built on collaborative filtering principles. While collaborative filtering proved effective in predicting ratings of movies by users based on historical community movie ratings, we would like to consider a content... Posted Sep 22 2009, 04:34 PM by n5712036 with | with no comments Filed under: Predictive Analytics, Pervasive DataRush, Multicore, parallel processing, Netflix, Netflix data, Netflix Prize, collaborative filter, data mining, knowledge discovery, KDD, dataflow, parallel data mining, SIGKDD, recommender, Zipfian, stopword, WordNet, information extraction, tokenizer, Zipf's law, text mining, stemmer

• Density-Based Clustering

  Data Mining and the knowledge discovery process seeks to find patterns in data. A common approach to pattern recognition is clustering . In recently published work ( KDD 2009), a dataflow implementation of kmeans and later two dimensional kmeans (co-clustering... Posted Sep 01 2009, 01:43 PM by n5712036 with | with 1 comment(s) Filed under: Netflix data, data mining, HPC, HPCwire, density-based clustering, knowledge discovery, clustering, KDD, dataflow, HALO project, TACC, UT

• Just returned from KDD’09 conference

  The Fifteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’09) in Paris, France was last week. The annual ACM SIGKDD conference is the premier international forum for data mining researchers and practitioners from academia... Posted Jul 13 2009, 11:26 AM by n5712036 with Filed under: Netflix, data mining, clustering, KDD, dataflow, RMSE, java, parallel data mining, KDDCup, colaborative filtering, SIGKDD, ACM, recommender, Pervasive