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data visualisation tool grafana  (Grafana Labs)

 
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    Structured Review

    Grafana Labs data visualisation tool grafana
    Overall architecture of the proposed AI-integrated monitoring system. The framework comprises two layers: (i) the Knowledge Plane, which consists of metric collection via Prometheus exporters for observation, embedding AI models for predictive analytics, and integration with <t>Grafana</t> and Alertmanager for <t>visualisation</t> and alert routing; and (ii) the Infrastructure Plane, which consists of network devices that generate <t>data</t> and require monitoring and maintenance. Optimisation denotes actions derived from AI predictions, such as early fault alerts and ticket prioritisation, enabling network engineers to optimise network performance after making changes based on these alerts. Directional arrows indicate data and control flows across the components.
    Data Visualisation Tool Grafana, supplied by Grafana Labs, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/data+visualisations/data+interactive+tool+visualization/pmc12526840-63-7-10
    Average 86 stars, based on 1 article reviews
    data visualisation tool grafana - by Bioz Stars, 2026-09
    86/100 stars

    Images

    1) Product Images from "AI and IoT-Driven Monitoring and Visualisation for Optimising MSP Operations in Multi-Tenant Networks: A Modular Approach Using Sensor Data Integration"

    Article Title: AI and IoT-Driven Monitoring and Visualisation for Optimising MSP Operations in Multi-Tenant Networks: A Modular Approach Using Sensor Data Integration

    Journal: Sensors (Basel, Switzerland)

    doi: 10.3390/s25196248

    Overall architecture of the proposed AI-integrated monitoring system. The framework comprises two layers: (i) the Knowledge Plane, which consists of metric collection via Prometheus exporters for observation, embedding AI models for predictive analytics, and integration with Grafana and Alertmanager for visualisation and alert routing; and (ii) the Infrastructure Plane, which consists of network devices that generate data and require monitoring and maintenance. Optimisation denotes actions derived from AI predictions, such as early fault alerts and ticket prioritisation, enabling network engineers to optimise network performance after making changes based on these alerts. Directional arrows indicate data and control flows across the components.
    Figure Legend Snippet: Overall architecture of the proposed AI-integrated monitoring system. The framework comprises two layers: (i) the Knowledge Plane, which consists of metric collection via Prometheus exporters for observation, embedding AI models for predictive analytics, and integration with Grafana and Alertmanager for visualisation and alert routing; and (ii) the Infrastructure Plane, which consists of network devices that generate data and require monitoring and maintenance. Optimisation denotes actions derived from AI predictions, such as early fault alerts and ticket prioritisation, enabling network engineers to optimise network performance after making changes based on these alerts. Directional arrows indicate data and control flows across the components.

    Techniques Used: Derivative Assay, Control

    Modular architecture of decentralised monitoring platform. Data flow of the monitoring pipeline work as the metric data is collected from exporters and processed through Prometheus instances hosted on Raspberry Pi edge nodes. Aggregated data is forwarded to the Mimir time-series database for long-term storage and AI model training. Trained models are containerised and deployed either centrally or at the edge for inference. Predictions are exposed as Prometheus metrics and visualised in Grafana dashboards. This architecture enables hybrid edge–cloud analytics, real-time alerting, and scalable tenant isolation.
    Figure Legend Snippet: Modular architecture of decentralised monitoring platform. Data flow of the monitoring pipeline work as the metric data is collected from exporters and processed through Prometheus instances hosted on Raspberry Pi edge nodes. Aggregated data is forwarded to the Mimir time-series database for long-term storage and AI model training. Trained models are containerised and deployed either centrally or at the edge for inference. Predictions are exposed as Prometheus metrics and visualised in Grafana dashboards. This architecture enables hybrid edge–cloud analytics, real-time alerting, and scalable tenant isolation.

    Techniques Used: Isolation

    Related Articles

    other:

    Article Title: AI and IoT-Driven Monitoring and Visualisation for Optimising MSP Operations in Multi-Tenant Networks: A Modular Approach Using Sensor Data Integration
    Article Snippet: The integration of this data into the data visualisation tool Grafana facilitates department-wise usage aggregation.

    Article Title: Towards rapid modeling and prototyping of indoor and outdoor monitoring applications
    Article Snippet: 13 Grafana, interactive visualization tool. https://grafana.com.

    Software:

    Article Title: Dynamically updating RAN applications in a RAN system
    Article Snippet: .. As shown, the system diagram 5300 includes an ingress controller 5315, an RMS UI 5305, an RMS API server 5310, an API cache 5320, a database 5325, a configuration management service 5330, a health check service 5335, an authentication/authorization service 5340 that works with Postgres 5345, data visualization dashboard software (e.g., Kibana) 5350, an analytics and interactive visualization web application endpoint (e.g., Grafana) 5355, a metric collection and caching frontend service (e.g., a Thanos Query Frontend) 5360, a storage system 5365 (e.g., a minIO storage system), a metric collection query service (e.g., a Thanos Query) 5370, elasticsearch and logstash 5375, and a set of one or more cRICs/dRICs 5380. ..



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    Image Search Results


    Overall architecture of the proposed AI-integrated monitoring system. The framework comprises two layers: (i) the Knowledge Plane, which consists of metric collection via Prometheus exporters for observation, embedding AI models for predictive analytics, and integration with Grafana and Alertmanager for visualisation and alert routing; and (ii) the Infrastructure Plane, which consists of network devices that generate data and require monitoring and maintenance. Optimisation denotes actions derived from AI predictions, such as early fault alerts and ticket prioritisation, enabling network engineers to optimise network performance after making changes based on these alerts. Directional arrows indicate data and control flows across the components.

    Journal: Sensors (Basel, Switzerland)

    Article Title: AI and IoT-Driven Monitoring and Visualisation for Optimising MSP Operations in Multi-Tenant Networks: A Modular Approach Using Sensor Data Integration

    doi: 10.3390/s25196248

    Figure Lengend Snippet: Overall architecture of the proposed AI-integrated monitoring system. The framework comprises two layers: (i) the Knowledge Plane, which consists of metric collection via Prometheus exporters for observation, embedding AI models for predictive analytics, and integration with Grafana and Alertmanager for visualisation and alert routing; and (ii) the Infrastructure Plane, which consists of network devices that generate data and require monitoring and maintenance. Optimisation denotes actions derived from AI predictions, such as early fault alerts and ticket prioritisation, enabling network engineers to optimise network performance after making changes based on these alerts. Directional arrows indicate data and control flows across the components.

    Article Snippet: The integration of this data into the data visualisation tool Grafana facilitates department-wise usage aggregation.

    Techniques: Derivative Assay, Control

    Modular architecture of decentralised monitoring platform. Data flow of the monitoring pipeline work as the metric data is collected from exporters and processed through Prometheus instances hosted on Raspberry Pi edge nodes. Aggregated data is forwarded to the Mimir time-series database for long-term storage and AI model training. Trained models are containerised and deployed either centrally or at the edge for inference. Predictions are exposed as Prometheus metrics and visualised in Grafana dashboards. This architecture enables hybrid edge–cloud analytics, real-time alerting, and scalable tenant isolation.

    Journal: Sensors (Basel, Switzerland)

    Article Title: AI and IoT-Driven Monitoring and Visualisation for Optimising MSP Operations in Multi-Tenant Networks: A Modular Approach Using Sensor Data Integration

    doi: 10.3390/s25196248

    Figure Lengend Snippet: Modular architecture of decentralised monitoring platform. Data flow of the monitoring pipeline work as the metric data is collected from exporters and processed through Prometheus instances hosted on Raspberry Pi edge nodes. Aggregated data is forwarded to the Mimir time-series database for long-term storage and AI model training. Trained models are containerised and deployed either centrally or at the edge for inference. Predictions are exposed as Prometheus metrics and visualised in Grafana dashboards. This architecture enables hybrid edge–cloud analytics, real-time alerting, and scalable tenant isolation.

    Article Snippet: The integration of this data into the data visualisation tool Grafana facilitates department-wise usage aggregation.

    Techniques: Isolation

    RT-qPCR validation of RNA-sequencing. Top 6 downregulated (lower expression in blood-exposed samples) and top 6 upregulated (higher expression in blood-exposed samples). All statistical analyses were performed with unpaired t-tests. ns = not significant, ns = not significant, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001, N = 3, α = 0.05.

    Journal: bioRxiv

    Article Title: Adult organotypic brain slice cultures recapitulate extracellular matrix remodelling in haemorrhagic stroke

    doi: 10.1101/2025.07.15.665021

    Figure Lengend Snippet: RT-qPCR validation of RNA-sequencing. Top 6 downregulated (lower expression in blood-exposed samples) and top 6 upregulated (higher expression in blood-exposed samples). All statistical analyses were performed with unpaired t-tests. ns = not significant, ns = not significant, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001, N = 3, α = 0.05.

    Article Snippet: All visualisation of RNA- sequencing data was performed using GraphPad Prism 10.3.

    Techniques: Quantitative RT-PCR, Biomarker Discovery, RNA Sequencing, Expressing