JITA

JITA Journal of Information Technology and Applications

Vol. 9 No. 2 (2019): JITA - APEIRON

Nedeljko Šikanjić, Zoran Ž. Avramović

NEW APPROACH OF STORING AND RETRIEVING LARGE DATA VOLUMES

Original scientific paper

DOI:https://doi.org/10.7251/JIT1902089S

Abstract

In today’s world of advanced informational technologies, society is facing a huge amount of data that is just getting impossible to store, process and analyze. In these big data volumes, some of the important information is being lost, that could help us improve the quality of personal and business life. This paper focus is on finding the best possible way of approaching this issue to find a feasible solution in increasing the efficiency and quality of data.

Keywords: Data Warehouse, Data Lake, Lambda architecture.

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.