OntOraculum: a semantic ontology for managing performance metrics in smart environments

This study presents OntOraculum, an ontology designed to organize and analyze performance metrics in smart environments. It categorizes metrics into Hardware, Network, Software, Energy, and SecurityAndReliability domains, defining properties such as dependencies, impacts, and trends to support predictive analysis and dynamic adjustments. Using Protégé for modeling, Semantic Web Rule Language (SWRL) rules facilitate automated inferences, while SPARQL Protocol and Resource Description Framework (RDF) Query Language (SPARQL) queries enable data retrieval from the ontology. Through validation queries, the ontology addresses questions related to network latency, energy efficiency, security, quality of service, and anomaly detection. By unifying performance metrics across diverse domains, OntOraculum enables comprehensive analysis of dependencies and interactions among metrics. The results demonstrate its practical applicability for monitoring, optimizing, and adapting systems in distributed environments.

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Publication Details

Journal
PeerJ Computer Science
Published
2026-10-28
DOI
https://doi.org/10.7717/peerj-cs.4078
Primary Topic
Semantic Web and Ontologies
Type
article
Field-Weighted Citation Impact
0.00
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article

OntOraculum: a semantic ontology for managing performance metrics in smart environments

Darlan Noetzold, Valderi Reis Quietinho Leithardt, Jorge Luís Victória Barbosa, Juan F. De Paz
PeerJ Computer Science
Semantic Web and Ontologies
article

OntOraculum: a semantic ontology for managing performance metrics in smart environments

Darlan Noetzold, Valderi Reis Quietinho Leithardt, Jorge Luís Victória Barbosa, Juan F. De Paz
article en

Abstract

This study presents OntOraculum, an ontology designed to organize and analyze performance metrics in smart environments. It categorizes metrics into Hardware, Network, Software, Energy, and SecurityAndReliability domains, defining properties such as dependencies, impacts, and trends to support predictive analysis and dynamic adjustments. Using Protégé for modeling, Semantic Web Rule Language (SWRL) rules facilitate automated inferences, while SPARQL Protocol and Resource Description Framework (RDF) Query Language (SPARQL) queries enable data retrieval from the ontology. Through validation queries, the ontology addresses questions related to network latency, energy efficiency, security, quality of service, and anomaly detection. By unifying performance metrics across diverse domains, OntOraculum enables comprehensive analysis of dependencies and interactions among metrics. The results demonstrate its practical applicability for monitoring, optimizing, and adapting systems in distributed environments.

PeerJ Computer ScienceVol. 12
Universidad de Salamanca (ES), Iscte – Instituto Universitário de Lisboa (PT), Universidade do Vale do Rio dos Sinos (BR)
Openalex Percentile: Top 14%
Semantic Web and Ontologies
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