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Knowledge Graph Based Hard Drive Failure Prediction.

Tek Raj Chhetri¹, Anelia Kurteva¹, Jubril Gbolahan Adigun²

¹Semantic Technology Institute (STI), Department of Computer Science, University of Innsbruck, 6020 Innsbruck, Austria.

Sensors (Basel, Switzerland)

|February 15, 2022

Summary

This summary is machine-generated.

Predicting hard drive failures is crucial for system reliability. This study combines machine learning and semantic technology, using knowledge graphs, to improve hard drive failure prediction accuracy.

Keywords:

failure prediction hard drive knowledge graphs machine learning predictive maintenance reliability

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Area of Science:

Computer Science
Data Science
Reliability Engineering

Background:

Hard drive failure can cause system failure and data loss, highlighting the importance of reliability.
Existing hard drive failure prediction methods predominantly use machine learning (ML) or semantic technology independently.
ML methods lack context-awareness, while semantic technology methods do not leverage ML's predictive capabilities.

Purpose of the Study:

To develop an improved hard drive failure prediction method.
To integrate the strengths of both machine learning and semantic technology.
To enhance the accuracy and context-awareness of hard drive failure predictions.

Main Methods:

Development of a novel knowledge graph-based approach for hard drive failure prediction.
Leveraging ontologies and knowledge graphs (KGs) for context-awareness.
Integrating machine learning algorithms for pattern recognition and prediction.

Main Results:

The proposed knowledge graph-based method demonstrates superior performance in hard drive failure prediction.
Achieved higher accuracy compared to existing state-of-the-art methods.
Successfully integrated context-awareness with predictive capabilities.

Conclusions:

Combining machine learning and semantic technology offers a more effective approach to hard drive failure prediction.
Knowledge graphs provide essential context, enhancing ML-based predictions.
The proposed method significantly improves system reliability and data loss prevention.