Leveraging the AWS Cloud to Create Scalable Research Performance AnalyticsHPCC Systems
SciVal provides real-time analysis of research performance at the institution and country level. Additionally, metrics can be computed for custom entities such as research areas, researcher groups and document sets. The underlying data used to calculate these metrics is from Elsevier’s Scopus abstract and citation database. Using this database as input, with over 60 million articles, HPCC systems are utilized to provide both real-time and offline calculations of a broad spectrum of metrics to evaluate research performance. HPCC Thor system is used to provide both daily updates and weekly recalculation of the entire metric set, and deploy index data to Roxie. A Java based controller application manages automated weekly and daily job flows. Job flow steps include running jobs on Thor, deploying data to Roxie and performing Roxie switching.
To meet the demands of growing article data, usage data, entities and functionality, Scival HPCC system was migrated to AWS.
This talk will focus on HPCC implementation on AWS, including:
a) Choosing AWS instance types for Thor and Roxie clusters
b) Installation and configuration of HPCC with CloudFormation templates on AWS EC2 instances.
c) AWS Virtual Private Cloud (VPC) and Security Groups to provide network and security layer for HPCC cluster.
d) AWS Autoscaling group and Elastic Network Interface (ENI) to automatically replace and configure terminated instance in the cluster.
e) AWS Elastic Load Balancer and HA Proxy for Roxie load balancing and switching.
f) Nagios and Ganglia to monitor system load and HPCC system processes.
g) Using AWS reserved instances to minimize cost.
Sreekanth Mopuru
Sreekanth is currently Consulting/Principal Software Engineer at Elsevier. He currently works in the Research Products organization on Elsevier’s SciVal product (scival.com). He is a senior member of the SciVal team providing design and implementation of analytics and metrics solutions utilizing the HPCC platform. He has worked for Elsevier for over 12 years in various software engineering roles. He led the recent effort in migration of SciVal’s HPCC system to the Amazon Cloud platform.
Exchange 2013 coexistence | Autodiscover infrastructure | Part 2/2 | 12#23
http://o365info.com/exchange-2013-coexistence-environment-autodiscover-infrastructure-part-22/
Reviewing the subject of - Autodiscover infrastructure in an Exchange 2013 coexistence environment (this is the second article, in a series of two articles).
Eyal Doron | o365info.com
Leveraging the AWS Cloud to Create Scalable Research Performance AnalyticsHPCC Systems
SciVal provides real-time analysis of research performance at the institution and country level. Additionally, metrics can be computed for custom entities such as research areas, researcher groups and document sets. The underlying data used to calculate these metrics is from Elsevier’s Scopus abstract and citation database. Using this database as input, with over 60 million articles, HPCC systems are utilized to provide both real-time and offline calculations of a broad spectrum of metrics to evaluate research performance. HPCC Thor system is used to provide both daily updates and weekly recalculation of the entire metric set, and deploy index data to Roxie. A Java based controller application manages automated weekly and daily job flows. Job flow steps include running jobs on Thor, deploying data to Roxie and performing Roxie switching.
To meet the demands of growing article data, usage data, entities and functionality, Scival HPCC system was migrated to AWS.
This talk will focus on HPCC implementation on AWS, including:
a) Choosing AWS instance types for Thor and Roxie clusters
b) Installation and configuration of HPCC with CloudFormation templates on AWS EC2 instances.
c) AWS Virtual Private Cloud (VPC) and Security Groups to provide network and security layer for HPCC cluster.
d) AWS Autoscaling group and Elastic Network Interface (ENI) to automatically replace and configure terminated instance in the cluster.
e) AWS Elastic Load Balancer and HA Proxy for Roxie load balancing and switching.
f) Nagios and Ganglia to monitor system load and HPCC system processes.
g) Using AWS reserved instances to minimize cost.
Sreekanth Mopuru
Sreekanth is currently Consulting/Principal Software Engineer at Elsevier. He currently works in the Research Products organization on Elsevier’s SciVal product (scival.com). He is a senior member of the SciVal team providing design and implementation of analytics and metrics solutions utilizing the HPCC platform. He has worked for Elsevier for over 12 years in various software engineering roles. He led the recent effort in migration of SciVal’s HPCC system to the Amazon Cloud platform.
Exchange 2013 coexistence | Autodiscover infrastructure | Part 2/2 | 12#23
http://o365info.com/exchange-2013-coexistence-environment-autodiscover-infrastructure-part-22/
Reviewing the subject of - Autodiscover infrastructure in an Exchange 2013 coexistence environment (this is the second article, in a series of two articles).
Eyal Doron | o365info.com
Application form for MathsGenius Leadership Institute's academy program for 2014. For 3 weeks, you will live on the MathsGenius Leadership Institute campus and study with the MGLI exceptional faculty and guest speakers from various industries and leading institutions of higher learning.
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High-Dimensional Network Estimation using ECLHPCC 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.
Discovery Analytics: Tracking Ebola SpreadHPCC 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.