JITA

JITA Journal of Information Technology and Applications

Vol. 11 No. 1 (2021): JITA - APEIRON

Dražen Marinković, Velimir Kojić, Zoran Ž. Avramović

Software Application Development Using Container Technology

Original scientific paper

DOI:https://doi.org/10.7251/JIT2101054M

Abstract

The paper will give an example and an overview of how we can set up and maintain web applications using a Docker. We will define what Docker is, what containers are and how to use them. Developers often find themselves in a situation where their program works properly on a computer in the laboratory environment in which the application was developed, but after installing the program on the production server, the program does not work as expected. In such circumstances, it is difficult for a programmer to determine why a program is not working. Docker solves this problem by placing applications and virtual containers that run on the same operating system.

Keywords: Docker, Devops,Virtual machine, Container technology.

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.