Web page characteristics of educational adaptive web sites
Vol. 3 No. 1 (2013): JITA – APEIRON Željko Eremić, Dragica Radosav Web page characteristics of educational adaptive web sites Original scientific paper DOI: https://doi.org/10.7251/JIT1301020E Download Article PDF Abstract Educational information about single topic may be found on many different website pages. Those web pages may have different roles, such as the display of information related to teaching, teaching content or routing to other web pages. Educational material can be placed on adaptive websites. Adaptive websites can customize their view and the structure on the basis of previously recorded user behavior. Documents on which visitors often end their navigation are called target documents, and users often visit waypost documents before visiting the target documents. Characteristics of different types of documents are being investigated in this paperwork. Also guidelines related to the design of such educational web sites are being provided. Keywords: Adaptive website, Waypost, Web design. 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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.
The Impact of Quantum Phenomena on the Complexity of Communication Systems
Vol. 3 No. 1 (2013): JITA – APEIRON Aleksandar Stojanović The Impact of Quantum Phenomena on the Complexity of Communication Systems Original scientific paper DOI: https://doi.org/10.7251/JIT1301005S Download Article PDF Abstract This publication put the accent on strategical problems in information transmission. The analysis is based on substantially different structure between classical (bit) and quantum information unit (qubit). The scientific methodology used in this publication is relatively new (single qubit transfer based on no-cloning theorem). Important part of publication is devoted to solving problems where quantum information processing offers much more prolific solutions than classical information processing. From practical point of view, the advances of quantum based information technologies have been presented. Keywords: quantum information, communication complexity, cryptography. 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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.
Crm Performances Accented With the Implementation of Data Warehousing and Data Mining Technologies
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 Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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.
Object-Oriented Analysis and Design for one Algorithm of Computational Geometry: Forward, Reverse and Round-Trip Engineering
Vol. 3 No. 2 (2013): JITA – APEIRON Muzafer H Saračević, Predrag S Stanimirović, Sead H Mašović Object-Oriented Analysis and Design for one Algorithm of Computational Geometry: Forward, Reverse and Round-Trip Engineering Original scientific paper DOI: https://doi.org/10.7251/JIT1302096S Download Article PDF Abstract Triangulation of the polygon is a fundamental algorithm in computational geometry. This paper considers techniques of object-oriented analysis and design as a new tool for solving and analyzing convex polygon triangulation. The triangulation is analyzed from three aspects: forward, reverse and round-trip engineering. We give a suggestion for improving the obtained software solution of the polygon triangulation algorithm using technique that combines UML modeling and Java programming. Keywords: Software engineering, Computational geometry, Triangulation of Polygons, Modeling in UML, Java. 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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.
Using Decision Tree Classifier for Analyzing Students’ Activities
Vol. 3 No. 2 (2013): JITA – APEIRON Snježana Milinković, Mirjana Maksimovići Using Decision Tree Classifier for Analyzing Students’ Activities Original scientific paper DOI: https://doi.org/10.7251/JIT1302087M Download Article PDF Abstract In this paper students’ activities data analysis in the course Introduction to programming at Faculty of Electrical Engineering in East Sarajevo is performed. Using the data that are stored in the Moodle database combined with manually collected data, the model was developed to predict students’ performance in successfully passing the final exam. The goal was to identify variables that could help teachers in predicting students’ performance and making specific recommendations for improving individual activities that could directly influence final exam successful passing. The model was created using decision tree classifier and experiments were performed using the WEKA data mining tool. The effect of input attributes on the model performances was analyzed and applying appropriate techniques a higher accuracy of the generated model was achieved. Keywords: decision tree, moodle, students’ performances, e-learning. 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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.
Enumeration, Ranking and Generation of Binary Trees Based on Level-Order Traversal Using Catalan Cipher Vectors
Vol. 3 No. 2 (2013): JITA – APEIRON Adrijan Božinovski, Biljana Stojčevska, Veno Pačovski Enumeration, Ranking and Generation of Binary Trees Based on Level-Order Traversal Using Catalan Cipher Vectors Original scientific paper DOI: https://doi.org/10.7251/JIT1302078B Abstract Download Article PDF In this paper, a new representation of a binary tree is introduced, called the Catalan Cipher Vector, which is a vector of elements with certain properties. It can be ranked using a special form of the Catalan Triangle designed for this purpose. It is shown that the vector coincides with the level-order traversal of the binary tree and how it can be used to generate a binary tree from it. Streamlined algorithms for directly obtaining the rank from a binary tree and vice versa, using the Catalan Cipher Vector during the processes, are given. The algorithms are analyzed for time and space complexity and shown to be linear for both.The Catalan Cipher Vector enables a straightforward determination of the position and linking for every node of the binary tree, since it contains information for both every node’s ancestor and the direction of linking from the ancestor to that node. Thus, it is especially well suited for binary tree generation. Using another structure, called a canonical state-space tableau, the relationship between the Catalan Cipher Vector and the level-order traversal of the binary tree is explained. Keywords: Enumeration, Rank, Generation, Binary tree, Level-order traversal, Catalan Cipher Vector, Canonical State-Space Tableau. 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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.
Comparative Analysis of Data Mining Techniques Applied to Wireless Sensor Network Data for Fire Detection
Vol. 3 No. 2 (2013): JITA – APEIRON Mirjana Maksimović, Vladimir Vujović Comparative Analysis of Data Mining Techniques Applied to Wireless Sensor Network Data for Fire Detection Original scientific paper DOI: https://doi.org/10.7251/JIT1302065M Download Article PDF Abstract Wireless sensor networks (WSN) are a rapidly growing area for research and commercial development with very wide range of applications. Using WSNs many critical events like fire can be detected earlier to prevent loosing human lives and heavy structural damages. Integration of soft computing techniques on sensor nodes, like fuzzy logic, neural networks and data mining, can significantly lead to improvements of critical events detection possibility. Using data mining techniques in process of patterns discovery in large data sets it’s not often so easy. A several algorithms must be applied to application before a suitable algorithm for selected data types can be found. Therefore, the selection of a correct data mining algorithm depends on not only the goal of an application, but also on the compatibility of the data set. This paper focuses on comparative analysis of various data mining techniques and algorithms and in that purpose three different experiments on WSN fire detection data are proposed and performed. The primary goal was to see which of them has the best classification accuracy of fuzzy logic generated data and is the most appropriate for a particular application of fire detection. Keywords: Analysis, Data Mining, Fire Detection, WEKA, WSN. 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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.
Full Text Search and Indexing in Languages With Two Alphabets
Vol. 4 No. 1 (2014): JITA – APEIRON Tijana Talić Full Text Search and Indexing in Languages With Two Alphabets Original scientific paper DOI: https://doi.org/10.7251/JIT1401041T Download Article PDF Abstract The languages spoken in Bosnia and Herzegovina use both Cyrillic and Latin equally. This is an additional problem with indexing and full text searching. In this paper, we are analyzing this problem. Using the tools available on PostgreSQL and ispell dictionaries, we made a solution. As part of the solutions, we created a dictionary of stop words, adjusted the affix file for both alphabets and from the list of words made functional vocabularies for indexing and searching. We made a full search configuration which is useful for indexing texts in both alphabets. Keywords: Semantic full-text search; Indexing; Artifi cial intelligence. 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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.
Customer Satisfaction as a Significant Measure of Successful ERP Implementation
Vol. 4 No. 1 (2014): JITA – APEIRON Bojan Ivetić, Tonćo Marušić, Dragica Radosav Customer Satisfaction as a Significant Measure of Successful ERP Implementation Original scientific paper DOI: https://doi.org/10.7251/JIT1401031I Download Article PDF Abstract The measuring of implemented ERP system’s efficiency is in any case multidimensional. Various researchers dedicated a lot of attention trying to find the best way to measure the success or the effectiveness of ERP solution. „Customer satisfaction“ as a measure represents the crucial point in creating the model for Measuring the success of implemented ERP systems and therefore it is the subject of this work. In this work we shall see what effect the other measurements will have on the „customer satisfaction“, respecting the correlation between particular crucial categories in creating the model of implemented ERP system’s success. Keywords: ERP, informational systems, success measurement, customer satisfaction. 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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.
The Application of Information and Communication Technologies in Dance Sport in Bosnia and Herzegovina
Vol. 4 No. 1 (2014): JITA – APEIRON Velibor Srdić, Milan Nešićć The Application of Information and Communication Technologies in Dance Sport in Bosnia and Herzegovina Original scientific paper DOI: https://doi.org/10.7251/JIT1401023S Download Article PDF Abstract The research was performed with the aim of determining the frequency and ways of application of information and communication technologies (ICT) in dance sport in Bosnia and Herzegovina. The research was conducted on the sample of 33 dance clubs, that is, their representatives, with the condition for the clubs to belong to one of the two national dance associations of Bosnia and Herzegovina. The data were collected via interviews, associations’ web pages and their archives. The research utilized analysis and induction method. The results showed weak application of ITC in everyday work of dance clubs, but they also indicated the appropriate usage of ICT by dance associations (as the “umbrella” organizations of dance sport) at dance competitions. In this sense, it is advisable to steer finances towards improvement of dance clubs’ equipment and IT training of employees. Keywords: information and communication technologies, dance sport, application. 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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.