This community innovation has been accepted at the 2025 DHIS2 Annual Conference
AI/ML-Powered DHIS2 Monitoring with OpenSearch
As DHIS2 deployments grow, real-time monitoring, security, and performance optimization become critical. We integrate OpenSearch to track system health and API performance using opensearch agents and beats. This setup collects data on server resource usage, API response times, log activity, and system metrics. To go beyond traditional monitoring, we are applying AI and Machine Learning (ML) in OpenSearch to: Detect anomalies in real-time (e.g., unauthorized access, unusual server loads). Predict trends in server resource consumption and traffic fluctuations. Enhance security monitoring, identifying potential cyber threats. Improve log analysis, classifying security incidents automatically. Enable semantic search for structured and unstructured data in OpenSearch. This AI-driven monitoring solution transforms DHIS2 infrastructure management from reactive to proactive, reducing downtime, improving security, and optimizing resource allocation. Health organizations, governments, and NGOs can adapt this scalable model to enhance digital health system performance, anticipate risks, and automate decision-making processes. Our project demonstrates the power of AI-driven observability in DHIS2 environments, making systems more resilient, efficient, and secure.
Primary Author: Daniel Castelao Suárez
Keywords:
DHIS2, OpenSearch, Machine Learning, AI, Anomaly Detection, Predictive Analytics, Monitoring, Performance
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