IoT Analytics Health Service Monitoring Performance Degradation
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Solution Overview
Problem
Industrial and healthcare analytics applications deployed on IoT platforms face performance deterioration due to unforeseen changes in data sets, asset configurations, and business processes, leading to reduced operational efficiency and accuracy.
Innovation Solution
A health determination service is implemented on a cloud platform to monitor and calculate the performance of IoT analytics by selecting relevant performance metrics, comparing expected and actual outputs, and sending alerts when performance falls below a predetermined threshold, thereby enabling timely notifications and corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If analytics applications are deployed on IoT platforms to analyze data from assets, then business value and operational efficiency are improved, but performance deterioration occurs over time due to unforeseen changes in data sets, asset configurations, and business processes
Solution Approach 1:
The system performs preliminary actions by establishing baseline performance metrics and expected outputs for analytics applications before deployment. It continuously monitors actual performance against these pre-established baselines, enabling early detection of performance deterioration before it significantly impacts operational efficiency. This proactive approach allows corrective measures to be taken before reliability issues fully manifest.
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing actual analytics output against expected outputs and performance baselines. When performance deterioration is detected, the system provides feedback through alerts and notifications to stakeholders, enabling timely corrective actions. This closed-loop feedback system ensures that performance issues are identified and addressed promptly, maintaining both productivity and reliability.
2Measurement precision
If multiple performance metrics are monitored to ensure analytics reliability, then detection accuracy is improved, but system complexity and resource consumption increase
Solution Approach 1:
The system segments performance monitoring into distinct components: baseline establishment, real-time metric collection, performance calculation, and alert generation. Each component handles specific aspects of performance monitoring independently, making the overall system more manageable and less complex. This segmentation allows the system to achieve high measurement precision through specialized functions while keeping the overall architecture modular and maintainable.
Solution Approach 2:
The system implements a universal monitoring framework that can handle multiple performance metrics and analytics applications through a single integrated platform. The same core infrastructure supports various analytics types by configuring different metrics and thresholds, eliminating the need for separate monitoring systems for each analytics application. This multi-functionality reduces system complexity while maintaining comprehensive performance detection accuracy.
Data Source
AI summary
The example embodiments are directed to a system and method for monitoring the health of analytical applications. In certain embodiments, these analytic applications may be a part of a broader IoT solution that may optionally be hosted on a cloud platform. In one example, the method includes receiving an output of an IoT analytic application deployed on a computing platform, selecting at least one performance metric based on a type of the application, calculating a performance of the application based on the selected performance metric, the received output of the application, and an expected output of the application, and in response to detecting the calculated performance of the application is below a predetermined threshold, outputting an alert to a user device. Accordingly, the health of an IoT analytic application can be monitored and an alert can be provided when the application operates below a predetermined threshold.