Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

QAV: querying entity-attribute-value metadata in a biomedical database

P M Nadkarni1

  • 1Department of Anesthesiology, Yale University School of Medicine, New Haven, CT 06510, USA. nadkarni@cs.yale.edu

Computer Methods and Programs in Biomedicine
|June 1, 1997
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Medical Informatics Training at Yale University School of Medicine.

Yearbook of medical informatics·2016
Same author

The Common Data Elements for cancer research: remarks on functions and structure.

Methods of information in medicine·2006
Same author

Automating identification of adverse events related to abnormal lab results using standard vocabularies.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2006
Same author

Metadata driven generation of reports for clinical studies.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2006
Same author

Integration of Web-based and PC-based clinical research databases.

Methods of information in medicine·2004
Same author

Approaches and informatics tools to assist in the integration of similar clinical research questionnaires.

Methods of information in medicine·2004
Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026
Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026
Same journal

Evaluation of surrogate endpoints for survival outcomes using the surrogate package in R.

Computer methods and programs in biomedicine·2026
Same journal

Relative spectral and frication-based descriptors as numerical indicators of place of articulation shifts in fricatives produced by Polish children.

Computer methods and programs in biomedicine·2026
Same journal

Leaflet resection improves valve expansion and hemodynamic performance in redo TAVI with balloon- and self-expanding transcatheter heart valve configurations.

Computer methods and programs in biomedicine·2026
Same journal

Spectral super-resolution for Parkinson's voice via representation-level methods under mixed-reality acquisition.

Computer methods and programs in biomedicine·2026
See all related articles

Entity-attribute-value (EAV) data organization presents retrieval challenges. The QAV application simplifies set-based querying for complex biomedical databases like Columbia MED, enhancing data access.

Area of Science:

  • Biomedical Informatics
  • Database Management
  • Knowledge Representation

Background:

  • Entity-attribute-value (EAV) data models are common in complex biomedical databases.
  • EAV's relational form complicates efficient set-based data retrieval for applications.
  • The Columbia MED dataset is a large, research-focused medical metadata repository.

Purpose of the Study:

  • To address the difficulties of set-based data retrieval in EAV organized biomedical databases.
  • To introduce QAV, a client-server application designed for efficient querying.
  • To enable effective data retrieval from the Columbia MED dataset.

Main Methods:

  • Developed a client-server application named QAV.
  • QAV features a graphical user interface for user interaction.

Related Experiment Videos

  • The application generates SQL statements for server-side data retrieval.

    • Main Results:

    • QAV facilitates set-based querying on EAV structured data.
    • The application successfully retrieves data from the Columbia MED dataset.
    • Simplifies complex data access for users of biomedical metadata repositories.

    Conclusions:

    • QAV effectively overcomes the limitations of EAV data retrieval.
    • The application enhances the usability of large biomedical metadata repositories.
    • QAV represents a practical solution for set-based querying in EAV databases.