JITA

JITA Journal of Information Technology and Applications

Vol. 7 No. 1 (2018): JITA - APEIRON

Dalibor Drljača, Branko Latinović, Dušan Starčević

MODELLING THE PROCESS OF IS AUDITING IN THE PUBLIC ADMINISTRATION USING UML DIAGRAMS

Original scientific paper

DOI: https://doi.org/10.7251/JIT1701032D

Abstract

Although information system audit is a very important business process, at present this is not obligatory in the public administration institutions in Bosnia and Herzegovina and Republic of Srpska. Due to the importance of this process, this paper proposes a model for auditing of information systems in the public administration institutions. The model intends to explain the audit process using a visual representation of the process with UML diagrams. UML is an internationally recognised language for business process modelling and has a number of advantages over other similar languages and standards. Therefore, UML is selected in modelling for modelling of information system auditing process in the public administration institutions.

Keywords: UML, auditing, information systems, public administration, business process modelling

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.