Knowledge Graph Based Hard Drive Failure Prediction

Chhetri, Tek Raj; Kurteva, Anelia; Adigun, Jubril Gbolahan; Fensel, Dieter

doi:10.3390/s22030985

Cited by 21 publications

(6 citation statements)

References 58 publications

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“…This, however, may not accurately reflect the actual failure pattern, as failures can occasionally occur abruptly, which we regard as a limitation of this study. The recent work by Chhetri et al [100] on failure prediction, which focuses on hard drive failure prediction using knowledge graphs [101], has demonstrated even greater effectiveness. Future work will involve expanding on this approach of combining system metrics based on knowledge graphs, as described in [100], in order to achieve even greater effectiveness, as well as implementing the production method.…”

Section: Discussionmentioning

confidence: 99%

A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence

Chhetri

Dehury

Lind

et al. 2022

BDCC

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Identifying and anticipating potential failures in the cloud is an effective method for increasing cloud reliability and proactive failure management. Many studies have been conducted to predict potential failure, but none have combined SMART (self-monitoring, analysis, and reporting technology) hard drive metrics with other system metrics, such as central processing unit (CPU) utilisation. Therefore, we propose a combined system metrics approach for failure prediction based on artificial intelligence to improve reliability. We tested over 100 cloud servers’ data and four artificial intelligence algorithms: random forest, gradient boosting, long short-term memory, and gated recurrent unit, and also performed correlation analysis. Our correlation analysis sheds light on the relationships that exist between system metrics and failure, and the experimental results demonstrate the advantages of combining system metrics, outperforming the state-of-the-art.

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Section: Discussionmentioning

confidence: 99%

A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence

Chhetri

Dehury

Lind

et al. 2022

BDCC

Self Cite

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“…Various XAI methods are summarized in [68] that could be leveraged for PdM in the SME domain. Similarly Graph based approaches could be leveraged in PdM as described in the survey [69], e.g., Knowledge graph [70] and virtual Graph [71] would be useful from an SME perspective to work with limited computing resources. Another state-of-the-art software architecture for Human-AI teaming [72] for smart factories could also be handy for adopting Human-Center AI-based PdM methods in SMEs.…”

Section: Best Practices (Rq4)mentioning

confidence: 99%

A Systematic Mapping Study of Predictive Maintenance in SMEs

et al. 2022

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The rapid growth of Industry 4.0 and predictive methods fostered a great potential for state-of-the-art techniques in the industrial sector, especially in smart factories. The equipment failure or system breakdowns during run time of a factory creates a severe problems towards impoverishment of the production system and destitution of the business. Predictive Maintenance (PdM) is a cost-saving and data driven technique to predict the maintenance time of in-service equipment or systems to reduce breakdown time and increase productivity. Although PdM is pragmatically adopted in large-scale industries, there is a lack of studies that map the PdM adoption in small and medium-sized enterprises (SMEs). In this systematic mapping study (SMS), we focus on predictive maintenance from an SME perspective to explore the field for researchers, scientists, and developers to comprehend the potential of PdM systems, their challenges, distinctive characteristics, and best practices in SMEs. Our study is based on four research questions comprised of demographic data, key challenges, distinctive characteristics, and best practices of predictive maintenance in SMEs. We found that the current literature on PdM is deficient in the SME domain, especially the financial side is vague. There is a huge potential for PdM in SMEs to design cost models and focus on data availability impediments. Management and monitoring of PdM and skilled personnel are also inadequate. Thus, we present a study that extracts the knowledge from the existing literature about PdM in SMEs, finds the research gap, and can assist in identifying the barriers and challenges of PdM adoption in SMEs.

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“…In the security and privacy domains, KGs have been successfully utilised for privacyenabled penalisation on the web [25], intelligent decision-making, fraud detection, prediction and tracing of cyber attacks (see [28,42,44,60]). Other domains such as manufacturing (e.g., [9]) and logarithmic law (e.g., [10,54]) have also significantly benefited from utilising KGs to bridge knowledge silos, semantically enrich data, highlight data dependencies and discover insights and new knowledge. Multiple ontologies for data sharing such as Consent and Data Management Model (CDMM) [19], Data Protection Vocabulary (DPV) 3 [40] and GConsent [39] have been built and are widely utilised [33].…”

Section: Introductionmentioning

confidence: 99%

What is in your cookie box? Explaining ingredients of web cookies with knowledge graphs

Bushati,

Rasmusen,

Kurteva

et al. 2024

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The General Data Protection Regulation (GDPR) has imposed strict requirements for data sharing, one of which is informed consent. A common way to request consent online is via cookies. However, commonly, users accept online cookies being unaware of the meaning of the given consent and the following implications. Once consent is given, the cookie “disappears”, and one forgets that consent was given in the first place. Retrieving cookies and consent logs becomes challenging, as most information is stored in the specific Internet browser’s logs. To make users aware of the data sharing implied by cookie consent and to support transparency and traceability within systems, we present a knowledge graph (KG) based tool for personalised cookie consent information visualisation. The KG is based on the OntoCookie ontology, which models cookies in a machine-readable format and supports data interpretability across domains. Evaluation results confirm that the users’ comprehension of the data shared through cookies is vague and insufficient. Furthermore, our work has resulted in an increase of 47.5% in the users’ willingness to be cautious when viewing cookie banners before giving consent. These and other evaluation results confirm that our cookie data visualisation approach and tool help to increase users’ awareness of cookies and data sharing.

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Knowledge Graph Based Hard Drive Failure Prediction

Cited by 21 publications

References 58 publications

A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence

A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence

A Systematic Mapping Study of Predictive Maintenance in SMEs

What is in your cookie box? Explaining ingredients of web cookies with knowledge graphs

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