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Related Experiment Video

Updated: May 26, 2026

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

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

Using ontology databases for scalable query answering, inconsistency detection, and data integration.

Paea Lependu¹, Dejing Dou

¹Computer and Information Science Department, University of Oregon, Eugene, OR 97403, USA.

Journal of Intelligent Information Systems

|December 14, 2011

Summary

This summary is machine-generated.

Ontology databases using triggers for forward-computed inferences improve query speed but incur hidden load-time costs. This method effectively answers ontology-based queries and detects inconsistencies, enabling data integration.

Related Experiment Videos

Last Updated: May 26, 2026

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

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

Area of Science:

Computer Science
Bioinformatics
Database Management

Background:

Ontology databases manage ontologies and their instances.
Reasoning over transitive closures requires efficient query processing.
Existing methods include unfolding views or propagating assertions using triggers.

Purpose of the Study:

Evaluate the performance of using triggers for forward-computing inferences in ontology databases.
Assess the trade-offs between query time, load time, and space.
Demonstrate practical applications in biomedicine and data integration.

Main Methods:

Utilized existing benchmarks to evaluate trigger-based inference propagation.
Forward-computed inferences to optimize query processing.
Applied methods to case studies in genetics and neuroscience.

Main Results:

Trigger-based forward inference significantly improves query time.
Initial benchmarks masked true load-time costs.
Ontology databases effectively answer ontology-based queries and detect inconsistencies.

Conclusions:

Forward-computing inferences using triggers offers performance benefits for ontology databases.
Load-time costs need careful consideration beyond standard benchmarks.
The approach supports effective querying, inconsistency detection, and data integration across distributed systems.