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EVOLUCIÓN: DE SISTEMAS EXPERTOS EN ENTORNOS CLOUD: TENDENCIAS Y PATRONES 2020-2024.

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Bibliographic Details
Title: EVOLUCIÓN: DE SISTEMAS EXPERTOS EN ENTORNOS CLOUD: TENDENCIAS Y PATRONES 2020-2024.
Alternate Title: EVOLUTION OF EXPERT SYSTEMS IN CLOUD ENVIRONMENTS: TRENDS AND PATTERNS 2020-2024.
Authors: Alfonso Obando-Cruz, Óscar1 oobando@istvr.edu.ec, Antonio Chalco-Méndez, Guillermo1 gchalco@istvr.edu.ec, Auxiliadora Mateo-Washbrum, Ivette1 imateo@istvr.edu.ec, Homero Campoverde-Nevarez, Lenin1 lcampoverde@istvr.edu.ec, Carolina Decimavilla-Alarcón, Diana1 ddecimavilla@istvr.edu.ec
Source: Revista Episteme & Praxis. sep-dic2025, Vol. 3 Issue 3, p145-160. 16p.
Subject Terms: *Machine learning, Expert systems, Cloud computing, Anomaly detection (Computer security), Medical informatics, Rule-based programming, Distributed computing
Abstract (English): This research systematically characterizes the evolution of expert systems in cloud environments during 2020-2024 through a descriptive-correlational approach analyzing 22 implementations documented in scientific literature. Findings reveal three main methodological categories: adaptive rule-based systems (45.5%), hybrid systems with machine learning (36.4%), and distributed systems with contextual inference (18.1%). Domain-specific analysis identifies medical applications as the most mature (40.9%), followed by anomaly detection (31.8%) and decision support (27.3%), with average accuracy rates of 86.4%, 97.1%, and 89.8% respectively. Analysis reveals consistent relationships between multimedia integration and enterprise adoption, as well as between security considerations and organizational scalability. Implementation challenges are categorized as regulatory (37.1% impact), technical (34.2%), and organizational (28.7%). Temporal evolution evidences three distinctive phases: adaptation (2020-2021), consolidation (2022-2023), and specialization (2023-2024). Systems incorporating regulatory compliance from design show substantially superior adoption rates, confirming the critical importance of regulatory considerations for scalability in cloud environments. [ABSTRACT FROM AUTHOR]
Abstract (Spanish): Esta investigación caracteriza sistemáticamente la evolución de sistemas expertos en entornos cloud durante 2020-2024 mediante un enfoque descriptivo-correlacional que analiza 22 implementaciones documentadas en literatura científica. Los hallazgos revelan tres categorías metodológicas principales: sistemas basados en reglas adaptativas (45.5%), sistemas híbridos con aprendizaje automático (36.4%), y sistemas distribuidos con inferencia contextual (18.1%). El análisis por dominios específicos identifica aplicaciones médicas como las más maduras (40.9%), seguidas por detección de anomalías (31.8%) y soporte a la decisión (27.3%), con precisiones promedio de 86.4%, 97.1% y 89.8% respectivamente. El análisis revela relaciones consistentes entre integración multimedia y adopción empresarial, así como entre consideraciones de seguridad y escalabilidad organizacional. Los desafíos de implementación se categorizan en regulatorios (37.1% de impacto), técnicos (34.2%) y organizacionales (28.7%). La evolución temporal evidencia tres fases distintivas: adaptación (2020-2021), consolidación (2022-2023) y especialización (2023-2024). Los sistemas que incorporan cumplimiento normativo desde el diseño muestran adopción sustancialmente superior, confirmando la importancia crítica de consideraciones regulatorias para escalabilidad en entornos cloud. [ABSTRACT FROM AUTHOR]
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Database: Education Research Complete
Description
Abstract:This research systematically characterizes the evolution of expert systems in cloud environments during 2020-2024 through a descriptive-correlational approach analyzing 22 implementations documented in scientific literature. Findings reveal three main methodological categories: adaptive rule-based systems (45.5%), hybrid systems with machine learning (36.4%), and distributed systems with contextual inference (18.1%). Domain-specific analysis identifies medical applications as the most mature (40.9%), followed by anomaly detection (31.8%) and decision support (27.3%), with average accuracy rates of 86.4%, 97.1%, and 89.8% respectively. Analysis reveals consistent relationships between multimedia integration and enterprise adoption, as well as between security considerations and organizational scalability. Implementation challenges are categorized as regulatory (37.1% impact), technical (34.2%), and organizational (28.7%). Temporal evolution evidences three distinctive phases: adaptation (2020-2021), consolidation (2022-2023), and specialization (2023-2024). Systems incorporating regulatory compliance from design show substantially superior adoption rates, confirming the critical importance of regulatory considerations for scalability in cloud environments. [ABSTRACT FROM AUTHOR]
ISSN:29608341