Identifying Common Cause Failures using Score Data Mining

In this study, we employed data mining to accurately evaluate the failure rate of secure computers, providing valuable data information for our decision-making layer.This technique is beneficial not only for our decision-making but also for the long-term operation of our systems.Through in-depth analysis, we discovered inherent connections among various failure events and their mutual impacts.These findings contribute to a deeper understanding of common cause failures in secure computer systems and prepare for enhancing their security.Establishing a robust information system is essential to meet the increasing demands, especially in the complex internet environment where secure digital computer systems face ever-growing challenges.

Authors

Publication Details

Journal
Journal of Digital Information Management
Published
2024-12-15
DOI
https://doi.org/10.6025/jdim/2024/22/4/137-142
Primary Topic
Imbalanced Data Classification Techniques
Type
article
Field-Weighted Citation Impact
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article

Identifying Common Cause Failures using Score Data Mining

Yonghui Ma
Journal of Digital Information Management
Imbalanced Data Classification Techniques
article

Identifying Common Cause Failures using Score Data Mining

Yonghui Ma
article en

Abstract

In this study, we employed data mining to accurately evaluate the failure rate of secure computers, providing valuable data information for our decision-making layer.This technique is beneficial not only for our decision-making but also for the long-term operation of our systems.Through in-depth analysis, we discovered inherent connections among various failure events and their mutual impacts.These findings contribute to a deeper understanding of common cause failures in secure computer systems and prepare for enhancing their security.Establishing a robust information system is essential to meet the increasing demands, especially in the complex internet environment where secure digital computer systems face ever-growing challenges.

Journal of Digital Information ManagementVol. 22(4)
Openalex Percentile: Top 78%
Imbalanced Data Classification Techniques
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