Estimation of the Data Storage System Internal State by Evaluating System Observables

Student: Karpov Maksim

Supervisor: Andrey Ustyuzhanin

Educational Programme: Data Science (Master)

Year of Graduation: 2020

Research on process mining and machine learning techniques has recently received a significant amount of attention by product development and management communities. Indeed, these techniques allow both an automatic process and activity discovery and thus are high added value services that help reusing knowledge to support decision-making. The global aim of this research is to minimize “anomalous” moments of life in complicated physical systems (e.g. SANs – storage area networks). Experimental results for detecting anomalies and evaluating internal state of the system were obtained and interpreted.

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