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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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Related Experiment Video

Updated: May 19, 2026

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases
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Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

Federated queries for comparative effectiveness research: performance analysis.

Ronald C Price1, Derick Huth, Jody Smith

  • 1Center for High Performance Computing, University of Utah, Salt Lake City, UT, USA.

Studies in Health Technology and Informatics
|September 4, 2012
PubMed
Summary
This summary is machine-generated.

Federated query performance was evaluated for the Scalable Architecture for Federated Translational Inquiries Network (SAFTINet). The caGrid Federated Query Engine demonstrated strong, nearly linear scalability, proving suitable for comparative effectiveness research (CER) grids.

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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

Related Experiment Videos

Last Updated: May 19, 2026

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
05:02

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

Area of Science:

  • Health Informatics
  • Computer Science
  • Bioinformatics

Background:

  • Federated query systems are crucial for distributed data analysis in research.
  • The Scalable Architecture for Federated Translational Inquiries Network (SAFTINet) proposes a novel architecture for such systems.
  • Evaluating the performance of federated query engines is essential for their practical application.

Purpose of the Study:

  • To assess the performance of federated queries within a simulated SAFTINet environment.
  • To determine the suitability of the caGrid Federated Query Engine (FQE) for comparative effectiveness research (CER) applications.
  • To provide insights into hardware requirements for implementing CER grids.

Main Methods:

  • Performance testing of federated queries using the caGrid FQE.
  • Simulating the SAFTINet architecture with varying network sizes (4 to 32 nodes).
  • Utilizing both physical hardware and virtual machines in a high-performance computing environment with large synthetic patient datasets.

Main Results:

  • The caGrid FQE exhibited nearly linear scalability as the number of partner nodes increased.
  • The system demonstrated capability and suitability for executing federated queries in CER.
  • Performance remained robust across different network configurations and hardware types.

Conclusions:

  • The caGrid FQE is a capable and scalable solution for federated queries in CER.
  • The findings support the viability of the SAFTINet architecture for translational research.
  • This study informs hardware specifications for deploying effective CER grids.