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Bc. Sergii Pylypenko

Master's thesis

Cognitive Alerting for Performance Measures

Cognitive Alerting for Performance Measures
Abstract:
The goal of this thesis is to study set of typical approaches of anomaly detection, analyze the logs provided by GoodData s.r.o., which are the source for the detection, select an optimal anomaly detection approach and provide a software implementing it.
Abstract:
Cílem této práce je prozkoumat seznam typických přístupů k detekci anomálií, zanalyzovat záznamy poskytnuté GoodData s.r.o., které jsou zdrojem pro odhalování anomálií, vybrat optimální přístup k detekci a poskytnout software, které bude provadet detekci.
 

Keywords

Outlier detection forecasting ARIMA Splunk anomaly time series Kalman filter Machine Learning MLToolkit R-squared Root Mean Square error.
 
Language used: English
Date on which the thesis was submitted / produced: 4. 1. 2017
Identifier: https://is.muni.cz/th/ponqp/

Thesis defence

  • Date of defence: 8. 2. 2017
  • Supervisor: doc. RNDr. Vlastislav Dohnal, Ph.D.
  • Reader: Ing. Martin Čermák, Ph.D.

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Masaryk University

Faculty of Informatics

Master programme / field:
Informatics / Information Technology Security

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