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Vol. 1 No. 2 (2011): JITA - APEIRON

Dušan Okanović, Milan Vidaković, Zora Konjović

Monitoring of Jee Applications and Performance Prediction

Original scientific paper

DOI: https://doi.org/10.7251/JIT1102136O

Abstract

This paper presents one solution for continuous monitoring of JEE application. In order to reduce overhead, Kieker monitoring framework was used. This paper presents the architecture and basic functionality of the Kieker framework and how it can be extended for adaptive monitoring of JEE applications. Collected data was used for analysis of application performance. In order to predict application performance, regression analysis was employed.

Keywords: continuous monitoring, Java, JMX, regression analysis.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.