Extracting Hidden Knowledge From Inference Models For Predictive Maintenance
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Solution Overview
Problem
Existing systems for managing data processing systems struggle to provide trustworthy failure predictions due to the lack of visibility into the underlying decision-making processes of inference models, leading to inefficiencies and potential system failures.
Innovation Solution
Implementing systems and methods that utilize inference modeling and log analysis to predict failures, while also extracting and storing hidden knowledge from inference models as structured knowledge attributes, enabling better interpretability and trustworthiness of predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inference models are used to predict failures, then prediction accuracy is improved, but interpretability of the prediction process deteriorates
Solution Approach 1:
The patent extracts hidden knowledge from the inference model by analyzing its internal decision-making processes and log data. This extraction creates a separate knowledge base that captures the model's reasoning logic, allowing the model to maintain its predictive accuracy while the extracted knowledge provides interpretability through structured attributes and decision rules.
Solution Approach 2:
The patent introduces an intermediary layer between the inference model and the user interface. This intermediary component translates the model's internal decisions into human-readable explanations by querying the extracted hidden knowledge, thereby bridging the gap between accurate prediction and interpretability without compromising either aspect.
2Reliability
If detailed log analysis is performed to improve prediction trustworthiness, then system reliability is improved, but processing time and complexity increase
Solution Approach 1:
The patent performs preliminary action by pre-processing and structuring log data into a knowledge base before actual prediction queries are made. This pre-organization of information allows the system to quickly retrieve relevant knowledge during prediction without performing time-consuming analysis in real-time, thus maintaining reliability while reducing processing time.
Solution Approach 2:
The patent creates a simplified representation (copy) of the complex log analysis process in the form of structured knowledge attributes and decision rules. This copy allows the system to answer prediction queries efficiently by querying the simplified knowledge structure rather than re-analyzing raw logs, maintaining trustworthiness while reducing computational overhead.
3Loss of information
If hidden knowledge is extracted from inference models, then interpretability is improved, but device complexity increases
Solution Approach 1:
The patent segments the system into distinct functional components: the inference model for prediction, the knowledge extraction module for interpreting the model's decisions, and the structured knowledge base for storing and serving information. This segmentation allows each component to be developed, maintained, and optimized independently, managing overall system complexity while improving interpretability.
Data Source
AI summary
Methods and systems for managing data processing systems are disclosed. A data processing system may include and depend on the operation of hardware and/or software components. Inference models may be implemented to predict future system infrastructure outcomes (e.g., component failures) using information recorded in logs that reflect the operation of the components. However, the models may be complex “black boxes” and may generate critical outcome predictions for downstream consumers without explanations of how the predictions are determined, resulting in downstream consumers having low confidence in the predictions. Therefore, hidden knowledge (e.g., structured knowledge attributes) of the models may be extracted and/or used to understand the underlying processes that the models use to predict the system infrastructure outcomes. The hidden knowledge may be stored in a repository and may be provided for downstream use in order to increase the likelihood of preventing and/or mitigating future data processing system failures.