Academic Journal
An intelligent data processing architecture for complex information systems: case studies in environmental and energy systems ; Інтелектуальна архітектура обробки даних для складних інформаційних систем: кейс-дослідження екологічних та енергетичних систем
| Τίτλος: | An intelligent data processing architecture for complex information systems: case studies in environmental and energy systems ; Інтелектуальна архітектура обробки даних для складних інформаційних систем: кейс-дослідження екологічних та енергетичних систем |
|---|---|
| Συγγραφείς: | Melnyk, A. M., Tymchyshyn, V. S., Popyk, Yu. I., Zabchuk, V. V., Fatiuk, V. I., Мельник, А. М., Тимчишин, В. С., Попик, Ю. І., Забчук, В. В., Фатюк, В. І. |
| Στοιχεία εκδότη: | ВНТУ |
| Έτος έκδοσης: | 2026 |
| Συλλογή: | Vinnytsia National Technical University (VTNU): Electronic Archive / Вінницький національний технічний університет |
| Θεματικοί όροι: | програмна архітектура, складні об’єкти, обробка даних, потокова аналітика, IoT, виявлення аномалій, екологічний моніторинг, software architecture, complex objects, data processing, stream analytics, anomaly detection, environmental monitoring |
| Περιγραφή: | This paper proposes an intelligent data processing architecture for complex environmental and energy systems operating under conditions of high dynamics, heterogeneous data sources, and large-scale information flows. The architecture integrates distributed computing, edge/cloud infrastructure, IoT, stream analytics, and AI/ML models to support real-time data integration, normalization, synchronization, and intelligent analysis. A distinctive feature of the proposed approach is the incorporation of an intelligent anomaly detection method for heterogeneous streaming data. The method is based on multi-component assessment of the system state, taking into account the statistical characteristics of data streams, AI/ML model outputs, contextual rules, data quality, and temporal delays. This enables the detection not only of threshold-based deviations, but also of complex anomalous states associated with atypical parameter combinations, disruptions in temporal dynamics, or inconsistencies with domain-specific constraints. Practical evaluation was conducted using an environmental monitoring system and a smart grid network as case studies. The results confirmed the performance, scalability, adaptability, and effectiveness of the proposed architecture under high-load conditions, as well as its suitability for developing intelligent real-time information systems. ; У статті запропоновано інтелектуальну архітектуру обробки даних для складних екологічних та енергетичних систем, що функціонують в умовах високої динамічності, гетерогенності джерел і значних обсягів інформаційних потоків. Архітектура поєднує distributed computing, edge/cloud infrastructure, IoT, потокову аналітику та AI/ML-моделі для інтеграції, нормалізації, синхронізації й інтелектуального аналізу даних у режимі реального часу. Особливістю підходу є включення інтелектуального методу виявлення аномалій у гетерогенних потокових даних. Метод ґрунтується на багатокомпонентному оцінюванні стану системи з урахуванням статистичних характеристик потоків, результатів ... |
| Τύπος εγγράφου: | article in journal/newspaper |
| Περιγραφή αρχείου: | application/pdf |
| Γλώσσα: | English |
| Relation: | Оптико-електроннi iнформацiйно-енергетичнi технологiї. № 1 : 41-56.; https://oeipt.vntu.edu.ua/index.php/oeipt/article/view/839; Zhang C., Patras P., Haddadi H. Deep Learning in Mobile and Wireless Networking: A Survey // IEEECommunications Surveys & Tutorials. 2020. Vol. 22(3). P. 2224–2287.; Chen Z., Chan I. C. C. Smart Cities and Quality of Life: A Quantitative Analysis of Citizens' Supportfor Smart City Development // Information Technology & People. 2023. Vol. 36(1). P. 263–285.; Khan Z., Anjum A., Soomro K., Tahir M. Towards Cloud Based Big Data Analytics for Smart FutureCities // Journal of Cloud Computing. 2020. Vol. 9(1). P. 1–24.; Ahmed E., Yaqoob I., Hashem I. A. T., et al. The Role of Big Data Analytics in Internet of Things //Computer Networks. 2021. Vol. 129. P. 459–471.; Aithal P. S. ICT and Digital Technology Based Solutions for Smart City Challenges and Opportunities// International Journal of Applied Engineering and Management Letters (IJAEML). 2022. Vol. 6(1). P.1–21.; Vedantham L. S., Zhou Y., Wu J. Information and Communications Technology (ICT) InfrastructureSupporting Smart Local Energy Systems: A Review // IET Energy Systems Integration. 2022. Vol.4(4). P. 460–472.; Breunig M. M., Kriegel H.-P., Ng R. T., Sander J. LOF: Identifying Density-Based Local Outliers //ACM Transactions on Database Systems. 2020. Vol. 45(2). P. 1–24.; Entezari A., Aslani A., Zahedi R., et al. Artificial Intelligence and Machine Learning in EnergySystems // Energy Reports. 2023. Vol. 9. P. 1045–1063.; Aleran A., Kumar R., Singh P., et al. Artificial Intelligence and the Internet of Things in EnergyPreservation Systems // Computing. 2025. Vol. 107. P. 1–25.; Lukashevych V. V., Vakaliuk T. A. Scientific Approaches to Big Data Processing Methods // ActualProblems of Automation and Information Technologies. 2025.; Artemchuk V., Iatsyshyn A., Kovach V., Popov O. Intelligent Analysis of Atmospheric Air MonitoringNetwork Data as a Tool for Environmental Safety Management of Urbanized Territories. 2019.; Das D. K., Sharma V., Gupta N., et al. Integrating IoT and AI for Sustainable Energy-Efficient SmartBuildings // Sustainability. 2025. Vol. 17(22). P. 10313–10335.; Singh A. R., Patel M., Verma S., et al. A Deep Learning and IoT-Driven Framework for Real-TimeSmart Grid Optimization // Scientific Reports. 2025. Vol. 15. P. 1–18.; Banad Y. M., Rahman M., Alharbi F., et al. Artificial Intelligence and Machine Learning for SmartGrids // Smart Energy. 2025. Vol. 18. P. 214–233.; Nikpour M., Rezaei H., Ahmadi A. Intelligent Energy Management in Smart Cities Using IntelligentAnalysis // arXiv preprint. 2023. P. 1–16.; Tymchyshyn B., Manzhula V. Mathematical and Algorithmic Support of an Intelligent SoftwareSystem for Proactive Urban Ecosystem Management // Measurement and Computing Devices inTechnological Processes. 2025. No. 4. P. 35–48.; Manzhula V., Tymchyshyn B. Semantic Modeling of Heterogeneous Sources of Harmful AtmosphericEmissions for Proactive Urban Ecosystem Management // Scientific Papers of Donetsk NationalTechnical University. Series: Informatics, Cybernetics and Computer Engineering. 2025. No. 2.; Zhang J., Tao D. Empowering Things with Intelligence: A Survey of the Progress, Challenges, andOpportunities in AIoT // arXiv preprint. 2020. P. 1–34.; Baccour E., Erbad A., Hamila R., Mohamed A. Pervasive AI for IoT Applications: A Survey onResource-Efficient Distributed Artificial Intelligence // arXiv preprint. 2021. P. 1–29.; U.S. Department of Energy. AI for Energy: Opportunities for a Modern Grid and Clean EnergyEconomy. Washington: U.S. Department of Energy, 2024. 74 p.; Bisikalo, O., Kharchenko, V., Kovtun, V., Krak, I., Pavlov, S. Parameterization of the StochasticModel for Evaluating Variable Small Data in the Shannon Entropy Basis, Entropy, 2023, 25(2), 184.; https://ir.lib.vntu.edu.ua//handle/123456789/52110; 004.89:004.75:004.62 |
| DOI: | 10.31649/1681-7893-2026-51-1-41-56 |
| Διαθεσιμότητα: | https://ir.lib.vntu.edu.ua//handle/123456789/52110 https://doi.org/10.31649/1681-7893-2026-51-1-41-56 |
| Αριθμός Καταχώρησης: | edsbas.A78E1157 |
| Βάση Δεδομένων: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://ir.lib.vntu.edu.ua//handle/123456789/52110# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Items | – Name: Title Label: Title Group: Ti Data: An intelligent data processing architecture for complex information systems: case studies in environmental and energy systems ; Інтелектуальна архітектура обробки даних для складних інформаційних систем: кейс-дослідження екологічних та енергетичних систем – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Melnyk%2C+A%2E+M%2E%22">Melnyk, A. M.</searchLink><br /><searchLink fieldCode="AR" term="%22Tymchyshyn%2C+V%2E+S%2E%22">Tymchyshyn, V. S.</searchLink><br /><searchLink fieldCode="AR" term="%22Popyk%2C+Yu%2E+I%2E%22">Popyk, Yu. I.</searchLink><br /><searchLink fieldCode="AR" term="%22Zabchuk%2C+V%2E+V%2E%22">Zabchuk, V. V.</searchLink><br /><searchLink fieldCode="AR" term="%22Fatiuk%2C+V%2E+I%2E%22">Fatiuk, V. I.</searchLink><br /><searchLink fieldCode="AR" term="%22Мельник%2C+А%2E+М%2E%22">Мельник, А. М.</searchLink><br /><searchLink fieldCode="AR" term="%22Тимчишин%2C+В%2E+С%2E%22">Тимчишин, В. С.</searchLink><br /><searchLink fieldCode="AR" term="%22Попик%2C+Ю%2E+І%2E%22">Попик, Ю. І.</searchLink><br /><searchLink fieldCode="AR" term="%22Забчук%2C+В%2E+В%2E%22">Забчук, В. В.</searchLink><br /><searchLink fieldCode="AR" term="%22Фатюк%2C+В%2E+І%2E%22">Фатюк, В. І.</searchLink> – Name: Publisher Label: Publisher Information Group: PubInfo Data: ВНТУ – Name: DatePubCY Label: Publication Year Group: Date Data: 2026 – Name: Subset Label: Collection Group: HoldingsInfo Data: Vinnytsia National Technical University (VTNU): Electronic Archive / Вінницький національний технічний університет – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22програмна+архітектура%22">програмна архітектура</searchLink><br /><searchLink fieldCode="DE" term="%22складні+об%27єкти%22">складні об’єкти</searchLink><br /><searchLink fieldCode="DE" term="%22обробка+даних%22">обробка даних</searchLink><br /><searchLink fieldCode="DE" term="%22потокова+аналітика%22">потокова аналітика</searchLink><br /><searchLink fieldCode="DE" term="%22IoT%22">IoT</searchLink><br /><searchLink fieldCode="DE" term="%22виявлення+аномалій%22">виявлення аномалій</searchLink><br /><searchLink fieldCode="DE" term="%22екологічний+моніторинг%22">екологічний моніторинг</searchLink><br /><searchLink fieldCode="DE" term="%22software+architecture%22">software architecture</searchLink><br /><searchLink fieldCode="DE" term="%22complex+objects%22">complex objects</searchLink><br /><searchLink fieldCode="DE" term="%22data+processing%22">data processing</searchLink><br /><searchLink fieldCode="DE" term="%22stream+analytics%22">stream analytics</searchLink><br /><searchLink fieldCode="DE" term="%22anomaly+detection%22">anomaly detection</searchLink><br /><searchLink fieldCode="DE" term="%22environmental+monitoring%22">environmental monitoring</searchLink> – Name: Abstract Label: Description Group: Ab Data: This paper proposes an intelligent data processing architecture for complex environmental and energy systems operating under conditions of high dynamics, heterogeneous data sources, and large-scale information flows. The architecture integrates distributed computing, edge/cloud infrastructure, IoT, stream analytics, and AI/ML models to support real-time data integration, normalization, synchronization, and intelligent analysis. A distinctive feature of the proposed approach is the incorporation of an intelligent anomaly detection method for heterogeneous streaming data. The method is based on multi-component assessment of the system state, taking into account the statistical characteristics of data streams, AI/ML model outputs, contextual rules, data quality, and temporal delays. This enables the detection not only of threshold-based deviations, but also of complex anomalous states associated with atypical parameter combinations, disruptions in temporal dynamics, or inconsistencies with domain-specific constraints. Practical evaluation was conducted using an environmental monitoring system and a smart grid network as case studies. The results confirmed the performance, scalability, adaptability, and effectiveness of the proposed architecture under high-load conditions, as well as its suitability for developing intelligent real-time information systems. ; У статті запропоновано інтелектуальну архітектуру обробки даних для складних екологічних та енергетичних систем, що функціонують в умовах високої динамічності, гетерогенності джерел і значних обсягів інформаційних потоків. Архітектура поєднує distributed computing, edge/cloud infrastructure, IoT, потокову аналітику та AI/ML-моделі для інтеграції, нормалізації, синхронізації й інтелектуального аналізу даних у режимі реального часу. Особливістю підходу є включення інтелектуального методу виявлення аномалій у гетерогенних потокових даних. Метод ґрунтується на багатокомпонентному оцінюванні стану системи з урахуванням статистичних характеристик потоків, результатів ... – Name: TypeDocument Label: Document Type Group: TypDoc Data: article in journal/newspaper – Name: Format Label: File Description Group: SrcInfo Data: application/pdf – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: Оптико-електроннi iнформацiйно-енергетичнi технологiї. № 1 : 41-56.; https://oeipt.vntu.edu.ua/index.php/oeipt/article/view/839; Zhang C., Patras P., Haddadi H. Deep Learning in Mobile and Wireless Networking: A Survey // IEEECommunications Surveys & Tutorials. 2020. Vol. 22(3). P. 2224–2287.; Chen Z., Chan I. C. C. Smart Cities and Quality of Life: A Quantitative Analysis of Citizens' Supportfor Smart City Development // Information Technology & People. 2023. Vol. 36(1). P. 263–285.; Khan Z., Anjum A., Soomro K., Tahir M. Towards Cloud Based Big Data Analytics for Smart FutureCities // Journal of Cloud Computing. 2020. Vol. 9(1). P. 1–24.; Ahmed E., Yaqoob I., Hashem I. A. T., et al. The Role of Big Data Analytics in Internet of Things //Computer Networks. 2021. Vol. 129. P. 459–471.; Aithal P. S. ICT and Digital Technology Based Solutions for Smart City Challenges and Opportunities// International Journal of Applied Engineering and Management Letters (IJAEML). 2022. Vol. 6(1). P.1–21.; Vedantham L. S., Zhou Y., Wu J. Information and Communications Technology (ICT) InfrastructureSupporting Smart Local Energy Systems: A Review // IET Energy Systems Integration. 2022. Vol.4(4). P. 460–472.; Breunig M. M., Kriegel H.-P., Ng R. T., Sander J. LOF: Identifying Density-Based Local Outliers //ACM Transactions on Database Systems. 2020. Vol. 45(2). P. 1–24.; Entezari A., Aslani A., Zahedi R., et al. Artificial Intelligence and Machine Learning in EnergySystems // Energy Reports. 2023. Vol. 9. P. 1045–1063.; Aleran A., Kumar R., Singh P., et al. Artificial Intelligence and the Internet of Things in EnergyPreservation Systems // Computing. 2025. Vol. 107. P. 1–25.; Lukashevych V. V., Vakaliuk T. A. Scientific Approaches to Big Data Processing Methods // ActualProblems of Automation and Information Technologies. 2025.; Artemchuk V., Iatsyshyn A., Kovach V., Popov O. Intelligent Analysis of Atmospheric Air MonitoringNetwork Data as a Tool for Environmental Safety Management of Urbanized Territories. 2019.; Das D. K., Sharma V., Gupta N., et al. Integrating IoT and AI for Sustainable Energy-Efficient SmartBuildings // Sustainability. 2025. Vol. 17(22). P. 10313–10335.; Singh A. R., Patel M., Verma S., et al. A Deep Learning and IoT-Driven Framework for Real-TimeSmart Grid Optimization // Scientific Reports. 2025. Vol. 15. P. 1–18.; Banad Y. M., Rahman M., Alharbi F., et al. Artificial Intelligence and Machine Learning for SmartGrids // Smart Energy. 2025. Vol. 18. P. 214–233.; Nikpour M., Rezaei H., Ahmadi A. Intelligent Energy Management in Smart Cities Using IntelligentAnalysis // arXiv preprint. 2023. P. 1–16.; Tymchyshyn B., Manzhula V. Mathematical and Algorithmic Support of an Intelligent SoftwareSystem for Proactive Urban Ecosystem Management // Measurement and Computing Devices inTechnological Processes. 2025. No. 4. P. 35–48.; Manzhula V., Tymchyshyn B. Semantic Modeling of Heterogeneous Sources of Harmful AtmosphericEmissions for Proactive Urban Ecosystem Management // Scientific Papers of Donetsk NationalTechnical University. Series: Informatics, Cybernetics and Computer Engineering. 2025. No. 2.; Zhang J., Tao D. Empowering Things with Intelligence: A Survey of the Progress, Challenges, andOpportunities in AIoT // arXiv preprint. 2020. P. 1–34.; Baccour E., Erbad A., Hamila R., Mohamed A. Pervasive AI for IoT Applications: A Survey onResource-Efficient Distributed Artificial Intelligence // arXiv preprint. 2021. P. 1–29.; U.S. Department of Energy. AI for Energy: Opportunities for a Modern Grid and Clean EnergyEconomy. Washington: U.S. Department of Energy, 2024. 74 p.; Bisikalo, O., Kharchenko, V., Kovtun, V., Krak, I., Pavlov, S. Parameterization of the StochasticModel for Evaluating Variable Small Data in the Shannon Entropy Basis, Entropy, 2023, 25(2), 184.; https://ir.lib.vntu.edu.ua//handle/123456789/52110; 004.89:004.75:004.62 – Name: DOI Label: DOI Group: ID Data: 10.31649/1681-7893-2026-51-1-41-56 – Name: URL Label: Availability Group: URL Data: https://ir.lib.vntu.edu.ua//handle/123456789/52110<br />https://doi.org/10.31649/1681-7893-2026-51-1-41-56 – Name: AN Label: Accession Number Group: ID Data: edsbas.A78E1157 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.31649/1681-7893-2026-51-1-41-56 Languages: – Text: English Subjects: – SubjectFull: програмна архітектура Type: general – SubjectFull: складні об’єкти Type: general – SubjectFull: обробка даних Type: general – SubjectFull: потокова аналітика Type: general – SubjectFull: IoT Type: general – SubjectFull: виявлення аномалій Type: general – SubjectFull: екологічний моніторинг Type: general – SubjectFull: software architecture Type: general – SubjectFull: complex objects Type: general – SubjectFull: data processing Type: general – SubjectFull: stream analytics Type: general – SubjectFull: anomaly detection Type: general – SubjectFull: environmental monitoring Type: general Titles: – TitleFull: An intelligent data processing architecture for complex information systems: case studies in environmental and energy systems ; Інтелектуальна архітектура обробки даних для складних інформаційних систем: кейс-дослідження екологічних та енергетичних систем Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Melnyk, A. M. – PersonEntity: Name: NameFull: Tymchyshyn, V. S. – PersonEntity: Name: NameFull: Popyk, Yu. I. – PersonEntity: Name: NameFull: Zabchuk, V. V. – PersonEntity: Name: NameFull: Fatiuk, V. I. – PersonEntity: Name: NameFull: Мельник, А. М. – PersonEntity: Name: NameFull: Тимчишин, В. С. – PersonEntity: Name: NameFull: Попик, Ю. І. – PersonEntity: Name: NameFull: Забчук, В. В. – PersonEntity: Name: NameFull: Фатюк, В. І. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2026 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
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