An Ontology-driven Cloud Platform for Intelligent Monitoring with Dashboard-centric Analytics and Domain Knowledge Integration
DOI:
https://doi.org/10.31861/sisiot2026.1.01010Keywords:
intelligent monitoring, dashboard analytics, domain knowledge integration, federated monitoring, self-adaptive monitoringAbstract
This paper presents an ontology-driven cloud platform for intelligent monitoring that unifies domain knowledge representation, cloud-native telemetry acquisition, multi-method analytics, and dashboard-centric decision support in a single adaptive environment. Unlike conventional monitoring solutions that mainly aggregate metrics, logs, and alerts, the proposed platform treats the monitored information system as a dynamic, domain-dependent object whose state must be observed, diagnosed, predicted, and semantically interpreted with respect to context, architecture, and operational constraints. The platform integrates domain ontologies, telemetry pipelines, analytical and modeling services, hierarchical monitoring nodes, and an interactive dashboard layer supporting descriptive, diagnostic, predictive, prescriptive, comparative, and context-adaptive analytics. The study is motivated by the transition from fragmented metric-oriented monitoring to knowledge-driven monitoring capable of combining heterogeneous data sources, domain semantics, and adaptive decision support. The architecture is organized as a layered cloud platform, comprising data acquisition, integration and streaming, storage, ontology and knowledge management, analytical services, hierarchical federated monitoring, self-adaptation, and dashboard interaction. A central feature of the platform is the integration of domain knowledge into the monitoring loop through ontological mappings, semantic enrichment of telemetry, rule-based event interpretation, and dashboard widgets grounded in domain concepts. This allows the platform not only to detect anomalies and degradation patterns, but also to explain them in the language of the target domain and to adapt monitoring configuration to changes in the monitored system state. The paper also outlines thematic use cases showing how the same platform core can be connected to different application domains through ontology resources and domain-specific analytical templates. The proposed approach provides a reference basis for semantically integrated monitoring platforms.
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