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Flavio Villanustre kicks off the 2015 HPCC Systems Engineering Summit Community Day
2015 HPCC Systems Summit Community Day
2015 HPCC Systems Summit Community Day
HPCC Systems
Kshitij Khare & Syed Rahman, University of Florida, present at the 2015 HPCC Systems Engineering Summit Community Day. In this presentation, we will discuss the motivation/theory behind CONCORD and its advantages over previous methods. In particular, we will discuss how the CONCORD estimate is superior to the empirical covariance matrix. We will end with an example detailing the implementation and use of the CONCORD algorithm in ECL. An exposure to multivariate statistics is helpful, but not necessary. Attendees should expect to come out with an understanding of sparse covariance estimation, its applications and how to use the CONCORD algorithm in ECL.
High-Dimensional Network Estimation using ECL
High-Dimensional Network Estimation using ECL
HPCC Systems
Flavio Villanustre, LexisNexis, closes out Community Day and shares a bit of the roadmap plans.
2015 HPCC Systems Engineering Summit Community Day Wrap-up
2015 HPCC Systems Engineering Summit Community Day Wrap-up
HPCC Systems
днз №268 презентація
днз №268 презентація
olga_ruo
презентация2
презентация2
olga_ruo
свята вшанування в знз району 2013
свята вшанування в знз району 2013
olga_ruo
150защита шіс
150защита шіс
olga_ruo
районний огляд конкурс
районний огляд конкурс
olga_ruo
Recommended
Flavio Villanustre kicks off the 2015 HPCC Systems Engineering Summit Community Day
2015 HPCC Systems Summit Community Day
2015 HPCC Systems Summit Community Day
HPCC Systems
Kshitij Khare & Syed Rahman, University of Florida, present at the 2015 HPCC Systems Engineering Summit Community Day. In this presentation, we will discuss the motivation/theory behind CONCORD and its advantages over previous methods. In particular, we will discuss how the CONCORD estimate is superior to the empirical covariance matrix. We will end with an example detailing the implementation and use of the CONCORD algorithm in ECL. An exposure to multivariate statistics is helpful, but not necessary. Attendees should expect to come out with an understanding of sparse covariance estimation, its applications and how to use the CONCORD algorithm in ECL.
High-Dimensional Network Estimation using ECL
High-Dimensional Network Estimation using ECL
HPCC Systems
Flavio Villanustre, LexisNexis, closes out Community Day and shares a bit of the roadmap plans.
2015 HPCC Systems Engineering Summit Community Day Wrap-up
2015 HPCC Systems Engineering Summit Community Day Wrap-up
HPCC Systems
днз №268 презентація
днз №268 презентація
olga_ruo
презентация2
презентация2
olga_ruo
свята вшанування в знз району 2013
свята вшанування в знз району 2013
olga_ruo
150защита шіс
150защита шіс
olga_ruo
районний огляд конкурс
районний огляд конкурс
olga_ruo
1останній дзвоник
1останній дзвоник
olga_ruo
останній дзвоник
останній дзвоник
olga_ruo
1кабінети математики .конкурс 2013
1кабінети математики .конкурс 2013
olga_ruo
ікт днз 193
ікт днз 193
olga_ruo
Kötü sunumun 12 yolu
Kötü sunumun 12 yolu
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Mike Yang, Principle Technology Architect, Infosys Ltd, China presents at the HPCC Systems Engineering Summit Community Day 2015
HPCC Systems Brings Deeper Customer Understanding and More Exact Social Marke...
HPCC Systems Brings Deeper Customer Understanding and More Exact Social Marke...
HPCC Systems
Jesse Shaw, LexisNexis Risk Solutions, presents at the 2015 HPCC Systems Summit Community Day. Accelerate the exploratory analytics process to rapidly produce valuable insights when approaching new business problems or untested data sources by leveraging HPCC Systems’ Knowledge Engineering Language (KEL). KEL enables the creation, organization and extraction of data dimensions with a fraction of the ECL source code previously required. This presentation will explain how the graph analytics KEL features can be used to track the spread of Ebola throughout the US.
Discovery Analytics: Tracking Ebola Spread
Discovery Analytics: Tracking Ebola Spread
HPCC Systems
Presentation delivered at Boston Data Festival September 2015. Data-intensive computing represents a new computing paradigm to address Big Data processing requirements using high-performance architectures supporting scalable parallel processing to allow government, commercial organizations, and research environments to process massive amounts of data and implement new applications previously thought to be impractical or infeasible. The fundamental challenges of data-intensive computing are managing and processing exponentially growing data volumes, significantly reducing associated data analysis cycles to support practical, timely applications, and developing new algorithms which can scale to search and process massive amounts of data. The open source HPCC (High-Performance Computing Cluster) Systems platform offers a unified approach to Big Data processing requirements: (1) a scalable, integrated computer systems hardware and software architecture designed for parallel processing of data-intensive computing applications, and (2) a new programming paradigm in the form of a high-level, declarative, data-centric programming language designed specifically for big data processing. This presentation explores the challenges of data-intensive computing from a programming perspective, and describes the ECL programming language and the HPCC architecture designed for data-intensive computing applications. HPCC is an alternative to the Hadoop platform, and ECL is compared to Pig Latin, a high-level language developed for the Hadoop MapReduce architecture.
Big data processing using HPCC Systems Above and Beyond Hadoop
Big data processing using HPCC Systems Above and Beyond Hadoop
HPCC Systems
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Mike Yang, Principle Technology Architect, Infosys Ltd, China presents at the HPCC Systems Engineering Summit Community Day 2015
HPCC Systems Brings Deeper Customer Understanding and More Exact Social Marke...
HPCC Systems Brings Deeper Customer Understanding and More Exact Social Marke...
HPCC Systems
Jesse Shaw, LexisNexis Risk Solutions, presents at the 2015 HPCC Systems Summit Community Day. Accelerate the exploratory analytics process to rapidly produce valuable insights when approaching new business problems or untested data sources by leveraging HPCC Systems’ Knowledge Engineering Language (KEL). KEL enables the creation, organization and extraction of data dimensions with a fraction of the ECL source code previously required. This presentation will explain how the graph analytics KEL features can be used to track the spread of Ebola throughout the US.
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Presentation delivered at Boston Data Festival September 2015. Data-intensive computing represents a new computing paradigm to address Big Data processing requirements using high-performance architectures supporting scalable parallel processing to allow government, commercial organizations, and research environments to process massive amounts of data and implement new applications previously thought to be impractical or infeasible. The fundamental challenges of data-intensive computing are managing and processing exponentially growing data volumes, significantly reducing associated data analysis cycles to support practical, timely applications, and developing new algorithms which can scale to search and process massive amounts of data. The open source HPCC (High-Performance Computing Cluster) Systems platform offers a unified approach to Big Data processing requirements: (1) a scalable, integrated computer systems hardware and software architecture designed for parallel processing of data-intensive computing applications, and (2) a new programming paradigm in the form of a high-level, declarative, data-centric programming language designed specifically for big data processing. This presentation explores the challenges of data-intensive computing from a programming perspective, and describes the ECL programming language and the HPCC architecture designed for data-intensive computing applications. HPCC is an alternative to the Hadoop platform, and ECL is compared to Pig Latin, a high-level language developed for the Hadoop MapReduce architecture.
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Big data processing using HPCC Systems Above and Beyond Hadoop
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Big data processing using HPCC Systems Above and Beyond Hadoop
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