Micro Service Recommendation Engine for Predictive Maintenance
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Solution Overview
Problem
Users face information overload when analyzing data for hardware assets, making it challenging to automatically identify the right combination of application and software tools to drill down into specific data corresponding to alerts, hindering effective exploration and analysis.
Innovation Solution
A recommendation engine utilizing machine learning algorithms analyzes user behavior and past explorations to predict and recommend relevant micro services for data analysis, providing a list of suitable tools to users during exploration, thereby automating the identification of necessary tools without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually explore and analyze data for hardware assets, then they can gain in-depth understanding of asset health, but the volume of data collected creates information overload and requires extensive user time and effort
Solution Approach 1:
The system performs self-service by automatically analyzing data patterns and generating relevant micro services without requiring manual user exploration. The machine learning model processes historical data and autonomously identifies appropriate analysis approaches, eliminating the need for users to manually navigate through vast amounts of data.
Solution Approach 2:
The machine learning model acts as an intermediary between the raw data and the user. It processes the volume of collected data, identifies patterns, and presents only the relevant micro services to the user, filtering out information overload while maintaining measurement precision.
2Adaptability or versatility
If various application and software tools are used for data exploration, then users can analyze hardware asset data, but it is challenging to automatically identify the right combination of tools for specific alerts
Solution Approach 1:
The system uses feedback from historical data and user interactions to continuously improve its recommendations. The machine learning model analyzes past successful explorations and adjusts its micro service selections accordingly, creating a feedback loop that improves adaptability while reducing the complexity of tool selection.
Solution Approach 2:
The system changes the parameter of data presentation by transforming raw data into structured micro services based on alert characteristics. The machine learning model adjusts which micro services are generated and presented, adapting the analysis approach to match the specific alert type and user needs.
3Quantity of substance
If the system provides comprehensive data collection for hardware assets, then users have complete information for analysis, but the data volume creates information overload when displayed to users
Solution Approach 1:
The system extracts only the essential information from the comprehensive data collection. The machine learning model identifies and extracts relevant patterns and micro services, separating useful information from redundant data, thereby maintaining data completeness while improving ease of operation.
Solution Approach 2:
The system segments the comprehensive data into discrete, manageable micro services. Instead of presenting raw data in its entirety, the data is divided into meaningful units (micro services) that are easier to process and interpret, reducing information overload while preserving analytical completeness.
Data Source
AI summary
A request is received to perform an exploration in a predictive and maintenance service application. A sequence of explorations is added in an evidence package. The evidence package includes the list of micro services. The sequence of explorations in the evidence package are analyzed. Based on the analysis, a user behavior corresponding to the performed exploration is identified. The user behavior is provided as input to the machine learning algorithm. Configuration data corresponding to the exploration and the evidence package is stored in a configuration database. The machine learning algorithm is executed in an execution engine. The execution engine is a micro service. Based on execution of the machine learning algorithm, the list of micro services is automatically identified as recommendations. The list of micro services is displayed as recommendations in the predictive and maintenance service application.