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

Branko Latinović

Extraction of Information in the Context of Business Inteligence

Original scientific paper

DOI: https://doi.org/10.7251/JIT1101054L

Abstract

Business Intelligence in the developed business systems allows better reasoning and decision making. ETL processes represent the most important processes in the system of Business Intelligence. It is about extracting, transforming, and filling a Data Warehouse with data which then transforms into data that is by its nature new and presented in a way that is meaningful and useful in an actual business organization. In conjunction with the methods of Information Extraction, knowledge is significantly expanded and given a completely new image. Intention is the collection of data that is available and processing the same in one place, regardless of whether the data was in a structural form or any other.

Keywords: Business Intelligence, Data Warehouse, Information Extraction.

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.