Knowledge Based Diagnostic Approach for Enterprise Storage Systems

Mamoutova, Olga V.; Uspenskiy, Mikhail B.; Sochnev, Anton V.; Smirnov, Sergey V.; Bolsunovskaya, Marina

doi:10.1109/sisy47553.2019.9111617

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Mamoutova et al [ 24 ] present an ontology-based approach to automatic analysis of data storage systems log files extending their work, a knowledge-based approach for enterprise storage system diagnosis using ontology [ 39 ]. Their work incorporates expert knowledge stored in a knowledge base as RDF (Resource Description Framework)/XML (Extensible Markup Language) format by converting it to the N-Quads format of the graph database.…”

Section: State Of the Artmentioning

confidence: 99%

Knowledge Graph Based Hard Drive Failure Prediction

Chhetri

Kurteva

Adigun

et al. 2022

Sensors

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The hard drive is one of the important components of a computing system, and its failure can lead to both system failure and data loss. Therefore, the reliability of a hard drive is very important. Realising this importance, a number of studies have been conducted and many are still ongoing to improve hard drive failure prediction. Most of those studies rely solely on machine learning, and a few others on semantic technology. The studies based on machine learning, despite promising results, lack context-awareness such as how failures are related or what other factors, such as humidity, influence the failure of hard drives. Semantic technology, on the other hand, by means of ontologies and knowledge graphs (KGs), is able to provide the context-awareness that machine learning-based studies lack. However, the studies based on semantic technology lack the advantages of machine learning, such as the ability to learn a pattern and make predictions based on learned patterns. Therefore, in this paper, leveraging the benefits of both machine learning (ML) and semantic technology, we present our study, knowledge graph-based hard drive failure prediction. The experimental results demonstrate that our proposed method achieves higher accuracy in comparison to the current state of the art.

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Section: State Of the Artmentioning

confidence: 99%

Knowledge Graph Based Hard Drive Failure Prediction

Chhetri

Kurteva

Adigun

et al. 2022

Sensors

View full text Add to dashboard Cite

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Score-VAE: Root Cause Analysis for Federated-Learning-Based IoT Anomaly Detection

Fan

Tang

et al. 2024

IEEE Internet Things J.

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Semantic Web and its role in facilitating ICT data sharing for the circular economy: An ontology survey

Kurteva,

McMahon,

Bozzon

et al. 2024

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The environmental pressure, CO2 emissions (including embodied energy) and delivery risks of our digital infrastructures are increasing. The exponentially growing digitisation of services that drive the transition from industry 4.0 to industry 5.0 has resulted in a rising materials demand for ICT hardware manufacturing. ICT devices such as laptops and data servers are being used on average for 3 and 4–5 years respectively (van Driel (2020)), while research shows that they should last 7 years before replacement (Journal of Cleaner Production 69 (2014), 10–16). A solution is to transition from a linear to a circular economy (CE), through which materials that were previously disposed of as waste are re-entered back into product life-cycles through processes such as reuse, recycling, remanufacturing, repurposing. However, the adoption of the CE in the ICT sector is currently limited due to the lack of tools that support knowledge exchange between sustainability, ICT and technology experts in a standardised manner and the limited data availability, accessibility and interoperability needed to build such tools. Further, the already existing knowledge of the domain is fragmented into silos and the lack of a common terminology restricts its interoperability and usability. These also lead to transparency and responsibility issues along the supply chain. For many years now, the Semantic Web has been known to provide solutions to such issues in the form of ontologies. Several ontologies for the ICT, materials and CE domains have been build and successfully utilised to support processes such as predictive maintenance. However, there is a lack of a systematic analysis of the existing ontologies in these domains. Motivated by this, we present a literature survey and analysis of, but not limited to, existing ontologies for ICT devices such as laptops, materials and the CE. In addition, we discuss the need for findable, accessible, interoperable, reusable (FAIR) data in the CE, different factors such as data privacy and security that affect this and the role of ontologies.

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Knowledge Based Diagnostic Approach for Enterprise Storage Systems

Cited by 3 publications

References 14 publications

Knowledge Graph Based Hard Drive Failure Prediction

Knowledge Graph Based Hard Drive Failure Prediction

Score-VAE: Root Cause Analysis for Federated-Learning-Based IoT Anomaly Detection

Semantic Web and its role in facilitating ICT data sharing for the circular economy: An ontology survey

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