Data Warehouse and Data Lake As Components of the Information Technology Platform of the Smart Region “Center of Europe”e

dc.citation.epage20
dc.citation.issue2
dc.citation.journalTitleОбчислювальні проблеми електротехніки
dc.citation.spage6
dc.citation.volume15
dc.contributor.affiliationUzhhorod National University
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorГолота, Олександр
dc.contributor.authorКут, Василь Іванович
dc.contributor.authorКунанець, Наталія Едуардівна
dc.contributor.authorHolota, Oleksandr
dc.contributor.authorKut, Vasyl
dc.contributor.authorKunanets, Nataliia
dc.coverage.placenameЛьвів
dc.date.accessioned2026-04-15T07:44:04Z
dc.date.created2025-02-27
dc.date.issued2025-02-27
dc.description.abstractУ статті проаналізовано сучасні підходи до використання сховищ та озер даних у побудові інформаційно-технологічних платформ розумних регіонів. Технології опрацювання даних у сховищах та озерах даних дозволяють інтегрувати, зберігати та аналізувати великі обсяги інформації, генерованої різними джерелами, зокрема оперативними транзакційними системами, сенсорами IoT та іншими даними, що надходять у реальному масштабі часу. Ефективне застосування сховищ даних відкриває можливості для покращення якості управління регіонами, оптимізації роботи всіх служб і підвищення життєвого рівня населення. Створення інформаційно-технологічних платформ розумних регіонів із використанням сховищ та озер даних є ключовим напрямом розвитку сучасних інформаційних технологій, що дозволяє ефективно використовувати їх не лише у густонаселених містах, але й для територій із складною географією, мультинаціональною структурою та різнорідними економічними галузями, таких як Закарпаття. У статті розглянуто особливості побудови сховищ та озер даних як складових інформаційної системи – від рівня оперативного опрацювання до створення вітрин даних, які забезпечують локалізований доступ до інформації для конкретних сфер впровадження, зокрема для ДСНС Закарпаття.
dc.description.abstractThe article analyzes modern approaches to the use of data warehouses and data lakes in the construction of information technology platforms for smart regions. Data processing technologies in data warehouses and data lakes allow for the integration, storage, and analysis of largeamounts of information generated by various sources, including operational transaction systems, IoT sensors, and other data received in real time. The effective use of data warehouses opens up opportunities to improve the quality of regional management, optimize the work of all services, and raise the standard of living of the population. The creation of information and technology platforms for smart regions using data warehouses and data lakes is a key direction in the development of modern information technologies, allowing them to be used effectively not only in densely populated cities, but also in areas with complex geography, multinational structures, and diverse economic sectors, such as Transcarpathia. The article discusses the features of building data warehouses and data lakes as components of an information system—from the level of operational processing to the creation of data showcases that provide localized access to information for specific areas of implementation, in particular for the State Emergency Service of Transcarpathia.
dc.format.extent6-20
dc.format.pages15
dc.identifier.citationHolota O. Data Warehouse and Data Lake As Components of the Information Technology Platform of the Smart Region “Center of Europe”e / Oleksandr Holota, Vasyl Kut, Nataliia Kunanets // Computational Problems of Electrical Engineering. — Lviv Politechnic Publishing House, 2025. — Vol 15. — No 2. — P. 6–20.
dc.identifier.citationenHolota O. Data Warehouse and Data Lake As Components of the Information Technology Platform of the Smart Region “Center of Europe”e / Oleksandr Holota, Vasyl Kut, Nataliia Kunanets // Computational Problems of Electrical Engineering. — Lviv Politechnic Publishing House, 2025. — Vol 15. — No 2. — P. 6–20.
dc.identifier.doidoi.org/10.23939/jcpee2025/02/006
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/124921
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofОбчислювальні проблеми електротехніки, 2 (15), 2025
dc.relation.ispartofComputational Problems of Electrical Engineering, 2 (15), 2025
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dc.relation.references[20] C. Thomsen and T. B. Pedersen, “pygrametl: A powerful programming framework for extract-transform-load programmers”, in Proc. 12th ACM Int. Workshop Data Warehousing and OLAP, pp. 49–56, 2009. doi: 10.1145/1651291.1651301.
dc.relation.referencesen[1] J. O. Palka, N. Kunanets, V. Pasichnyk, O. Matsiuk, and S. Matsiuk, "Comparative Analysis of Smart City Platforms", CEUR Workshop Proceedings, vol. 3403, pp. 487–499, 2023. [Online]. Available: https://ceur-ws.org/
dc.relation.referencesen[2] J. Colding, M. Colding, and S. Barthel, "The smart city model: A new panacea for urban sustainability or unmanageable complexity", Environment and Planning B: Urban Analytics and City Science, vol. 47, no. 1, pp. 179–187, 2020. [Online]. Available: http://dx.doi.org/10.1177/2399808318763164
dc.relation.referencesen[3] E. Ho, "Smart subjects for a smart nation? Governing (smart) mentalities in Singapore", Urban Studies, vol. 54, no. 13, pp. 3101–3118, 2017. [Online]. Available: http://dx.doi.org/10.1177/004209801666430
dc.relation.referencesen[4] A. Meijer and M. P. R. Bolivar, "Governing the Smart city: A review of the literature on smart urban governance", International Review of Administrative Sciences, vol. 82, no. 2, pp. 392–408, 2016. [Online]. Available: http://dx.doi.org/10.1177/0020852314564308
dc.relation.referencesen[5] A. Nambiar and D. Mundra, "An Overview of Data Warehouse and Data Lake in Modern Enterprise Data Management", Big Data and Cognitive Computing, vol. 6, no. 4, p. 132, 2022. doi: 10.3390/bdcc6040132.
dc.relation.referencesen[6] M. Anthony, P. Martins, F. Caldeira, and F. Sá, "An Evaluation of How Big-Data and Data Warehouses Improve Business Intelligence Decision Making", in Proc. Conference on Information Systems and Technologies, Cham: Springer, pp. 609–619, 2020. doi: 10.1007/978-3-030-45688-7_61.
dc.relation.referencesen[7] I. Megdiche, F. Ravat, and Y. Zhao, "A Use Case of Data Lake Metadata Management", in Data Lakes 2, pp. 97–122, 2020.
dc.relation.referencesen[8] M. A. Farnum et al., A Dimensional Warehouse for Integrating Operational Data from Clinical Trials, Database, 2019.
dc.relation.referencesen[9] W. H. Inmon, "Building the Data Warehouse", 4th ed., vol. 13, no. 401. 2005.
dc.relation.referencesen[10] E. Saddad, A. El-Bastawissy, O. Hegazy, and M. Hazman, "Towards an alternative Data Warehouses Architecture", in Proc. 14th International Conference on Hybrid Intelligent Systems (HIS 2014), Kuwait, , vol. 6, pp. 48–53, Dec. 14–16, 2014
dc.relation.referencesen[11] S. H. A. El-Sappagh, A. M. A. Hendawi, and A. H. El Bastawissy, "A proposed model for data warehouse ETL processes", Journal of King Saud University – Computer and Information Sciences, vol. 23, no. 2, pp. 91–104, 2011. doi: 10.1016/j.jksuci.2011.05.005.
dc.relation.referencesen[12] H. L. H. S. Warnars, L. S. Warnars, A. Ramadhan, T. Siswanto, and A. Doucet, "Data warehouse design for firefighters operational at the DKI Jakarta fire department", TEM Journal, vol. 13, no. 1, pp. 365–376, 2024. doi: 10.18421/TEM131-38.
dc.relation.referencesen[13] V. Belov, A. N. Kosenkov, and E. Nikulchev, "Experimental characteristics study of data storage formats for data marts development within data lakes", Applied Sciences (Switzerland), vol. 11, no. 18, p. 8651, 2021, doi: 10.3390/app11188651.
dc.relation.referencesen[14] K. Krishnan, Data Warehousing in the Age of Big Data, Elsevier Inc., 2013.
dc.relation.referencesen[15] R. G. Goss and K. Veeramuthu, "Heading towards big data: building a better data warehouse for more data, more speed, and more users", in Proc. ASMC 2013 SEMI Advanced Semiconductor Manufacturing Conference, 2013, pp. 220–225.
dc.relation.referencesen[16] A. A. Harby and F. Zulkernine, "From Data Warehouse to Lakehouse: A Comparative Review", in Proc. 2022 IEEE International Conference on Big Data (Big Data), 2022. doi: 10.1109/BigData55660. 2022.10020719.
dc.relation.referencesen[17] A. Sebaa, F. Chikh, A. Nouicer, and A. Tari, "Research in Big Data Warehousing using Hadoop", J. Inf. Syst. Eng. Manag., vol. 2, no. 2, pp. 1–5, 2017.
dc.relation.referencesen[18] D. Amo, P. Gómez, L. Hernández-Ibáñez, and D. Fonseca, "Educational Warehouse: Modular, Private and Secure Cloudable Architecture System for Educational Data Storage, Analysis and Access", Appl. Sci., vol. 11, p. 806, 2021. doi: 10.3390/app11020806.
dc.relation.referencesen[19] N. Gür, J. Nielsen, K. Hose, and T. B. Pedersen, "GeoSemOLAP: Geospatial OLAP on the Semantic Web made easy", in Proc. 26th Int. Conf. World Wide Web Companion, New York, NY, USA: ACM, pp. 213–217, 2017. doi: 10.1145/3041021.3054731
dc.relation.referencesen[20] C. Thomsen and T. B. Pedersen, "pygrametl: A powerful programming framework for extract-transform-load programmers", in Proc. 12th ACM Int. Workshop Data Warehousing and OLAP, pp. 49–56, 2009. doi: 10.1145/1651291.1651301.
dc.relation.urihttps://ceur-ws.org/
dc.relation.urihttp://dx.doi.org/10.1177/2399808318763164
dc.relation.urihttp://dx.doi.org/10.1177/004209801666430
dc.relation.urihttp://dx.doi.org/10.1177/0020852314564308
dc.rights.holder© Національний університет “Львівська політехніка”, 2025
dc.subjectdata warehouse
dc.subjectdata lake
dc.subjectdata showcases
dc.subjectsmart region
dc.subjectbig data
dc.titleData Warehouse and Data Lake As Components of the Information Technology Platform of the Smart Region “Center of Europe”e
dc.title.alternativeСховище та озеро даних як складові інформаційно-технологічної платформи розумного регіону “Центр Європи”
dc.typeArticle

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