Thesis Details
Detekce anomálií na základě stavu RQA systému
The aim of the theses is to design and implement a machine learning model for anomaly detection in Y Soft's RQA system. Owing to the microservice architecture, an anomaly is considered to be a recurring occurrence of outliers in durations of service requests or a considerable variance in error rate. The thesis outlines the current data collection process in the system and defines what kind of data describe the state of the system. It devises a suitable format of data storage for its subsequent analysis. It presents algorithms commonly used to solve anomaly detection problems. The anomaly detection is designed and implemented using cluster analysis and statistical methods. Finally, the thesis evaluates the quality of the detection and the achieved results.
data mining, data analysis, machine learning, anomaly detection, cluster analysis, statistics, .NET, monitoring
Burgetová Ivana, Ing., Ph.D. (DIFS FIT BUT), člen
Fučík Otto, doc. Dr. Ing. (DCSY FIT BUT), člen
Hrubý Martin, Ing., Ph.D. (DITS FIT BUT), člen
Španěl Michal, Ing., Ph.D. (DCGM FIT BUT), člen
@bachelorsthesis{FITBT23935,
author = "Jan Lorenc",
type = "Bachelor's thesis",
title = "Detekce anom\'{a}li\'{i} na z\'{a}klad\v{e} stavu RQA syst\'{e}mu",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2021,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/23935/"
}