Failure Prediction Citations for Inference Model Trust
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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 mismanagement of system failures.
Innovation Solution
The implementation of inference modeling and log analysis, combined with the extraction of hidden knowledge in the form of structured knowledge attributes, allows for the interpretation and validation of failure predictions, enhancing trustworthiness and efficiency in managing data processing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inference models are used for failure prediction, then prediction capability is improved, but trustworthiness and interpretability deteriorate due to lack of visibility into decision-making processes
Solution Approach 1:
The patent introduces citation data as an intermediary element that mediates between the inference model's internal decision-making process and the external user. The citation data includes references to training data samples, model confidence scores, and explanation text that collectively serve as a bridge, allowing users to verify and understand the basis of predictions without exposing the complex internal workings of the inference model. This resolves the contradiction by maintaining prediction accuracy while providing sufficient transparency for trustworthiness.
2Measurement precision
If complex inference models are deployed, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the failure prediction system into distinct functional components: the inference model engine, the citation data generation module, and the presentation layer. By separating the complex inference model from its explanation and verification mechanisms, the system manages complexity through modular architecture. Each component can be independently developed, tested, and maintained, reducing the overall system complexity burden while preserving prediction accuracy.
3Reliability
If manual validation of predictions is performed, then trustworthiness is improved, but time consumption and productivity deteriorate
Solution Approach 1:
The patent implements self-service validation where the inference model automatically generates citation data including confidence scores, training data references, and explanation text. This self-generated documentation allows users to perform rapid validation without requiring manual inspection of model internals or extensive technical expertise. The system serves its own validation needs through automated citation generation, significantly improving productivity while maintaining reliability.
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 used to generate citations for the outcome predictions to provide references to previous cases (e.g., historic data) from which the outcome predictions are based.