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Vol. 3 No. 2 (2013): JITA - APEIRON

Ines Isaković

Crm Performances Accented With the Implementation of Data Warehousing and Data Mining Technologies

Original scientific paper

DOI: https://doi.org/10.7251/JIT1302107I

Abstract

Customer Relationship Management (CRM) has become more and more a key strategy for large and small businesses. It supports marketing, sales, services and involves direct and indirect customer interaction. Customers are put into the center of the business, because they represent an asset and profit for any company. Customers need to be satisfied in order to be loyal. A company can achieve that by meeting customer’s needs and expectations. In order to perform both for the benefit of the customer and for itself, a company has to use all the positive advantages of IS technologies that support CRM including data warehouses and data mining, that are clearly presented in this paper

Keywords: Customer Relationship Management (CRM), Data Warehousing (DW), Data Mining (DM).

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.