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Vol. 2 No. 2 (2012): JITA - APEIRON

Nedim Smailović

Data Visualization on Information Tables - Dashboards

Original scientific paper

DOI: https://doi.org/10.7251/JIT1202068S

Abstract

Today’s level of the information technology development allows gathering of large amount of data relevant for a company. The issue of way and method of data gathering has been solved, however, at the same time the need also arises for extraction of the most important ones, since the quantity of the input data itself is not enough. In the contemporary business activities, being pursued in the circumstances of high competition, changes, speed and risk, management of the company may be compared to driving a fast car. Therefore, the information tables – dashboards have been created for the purpose of data processing as an analogy to the car dashboard, which, by means of several indicators, provides for the driver an instant insight into various data related to driving and the engine running. Display of the data in the form of charts and diagrams undoubtedly helps in obtaining new knowledge however, an individual visual displaying is seldom sufficient thus they should be combined in different variants. The work presents concrete examples of the dashboards creation both locally and globally.

Keywords: Dashboard, information tables, business inteligence (BI), visual language, charts and diagrams.

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.