A Mechanism for Managing Algorithms for Building Data Storefronts in Intelligent Transport Systems
( Pp. 175-185)
More about authors
Gorodnichev Mikhail G.
Cand. Sci. (Eng.), Associate Professor; Dean, Faculty of Information Technology
Moscow Technical University of Communications and Informatics (MTUCI)
Moscow, Russian Federation Polyantseva Ksenia A. Cand. Sci. (Eng.); associate professor, Department of Data Mining; Moscow Technical University of Communications and Informatics (MTUCI); Moscow, Russian Federation Gryaznov Nikolaу A. Department of Mathematical Cybernetics and Information Technology; Moscow Technical University of Communications and Informatics (MTUCI); Moscow, Russian Federation.
Moscow Technical University of Communications and Informatics (MTUCI)
Moscow, Russian Federation Polyantseva Ksenia A. Cand. Sci. (Eng.); associate professor, Department of Data Mining; Moscow Technical University of Communications and Informatics (MTUCI); Moscow, Russian Federation Gryaznov Nikolaу A. Department of Mathematical Cybernetics and Information Technology; Moscow Technical University of Communications and Informatics (MTUCI); Moscow, Russian Federation.
Abstract:
The article considers the task of increasing the flexibility and manageability of the processes of building data storefronts in intelligent transport systems. The relevance of the research is due to the need for rapid changes in data processing algorithms in the context of dynamically changing requirements and a large volume of incoming information from transport infrastructure and telematics sources. A mechanism for managing algorithms for calculating data marts is proposed, providing centralized storage, validation, and dynamic application of computational rules without modifying the main code of ETL processes. The architecture of the software solution has been developed, including a user interface, a server application, and a repository of rules in a database, as well as the integration of the mechanism into the processes of building data marts. A mathematical model of the application of computational rules is presented, formalizing the process of selecting and composing data processing functions. Functional testing of the developed mechanism was carried out, which confirmed the correctness of its operation and the possibility of using it in various operating modes, including the pilot implementation of algorithms. The practical significance of the work lies in the possibility of using the proposed mechanism in intelligent transport information systems to reduce the time needed to implement algorithm changes and increase the reliability of data processing processes.
How to Cite:
Gorodnichev M.G., Polyantseva K.A., and Gryaznov N.A. A mechanism for managing algorithms for building data storefronts in intelligent transport systems. Computational Nanotechnology. 13, 1 (2026), 175–185. DOI: 10.33693/2313-223X-2026-13-1-175-185. EDN: MHSMFK
Reference list:
Polyantseva K.A. High-load platform for aggregation and analysis of unstructured data on the state of the roadway. Automation in Industry. 2022. No. 5. Pp. 32–37. (In Rus.). DOI: 10.25728/avtprom.2022.05.09.
Polyantseva K.A., Egorova K.O. Automation of big data processing processes using DevOps. Economics and Quality of Communication Systems. 2025. No. 4 (38). Pp. 107–118. (In Rus.)
Beese J., Aier S., Haki K., Winter R. The impact of enterprise architecture management on information systems architecture complexity. European Journal of Information Systems. 2023. Vol. 32. No. 6. Pp. 1070–1090. DOI: 10.1080/0960085X.2022.2103045.
Dibouliya A. Self-serving data marts orchestrated by AutoML-Governed pipelines. International Journal of Environmental Sciences. 2025. Pp. 65–82. DOI: 10.64252/p9cqj306.
Gong T., Zhu L., Yu F.R., Tang T. Edge intelligence in intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems. 2023. Vol. 24. No. 9. Pp. 8919–8944. DOI: 10.1109/TITS.2023.3275741.
Haque M.A., Ahmad S., Alanazi S., John A. IoT-based data analysis and security for intelligent transportation system in smart cities. In: Synergies in Data Analytics and Cyber Security. Proceedings of the International Conference (DACS 2024) / D. Puthal, B.K. Panigrahi, N. Ray, Z. Ding (eds.). Singapore: Springer, 2026. Pp. 62–75. DOI: 10.1007/978-981-95-2680-2_62.
Narayanan P.K. Orchestrating data engineering pipelines using apache airflow. In: Data engineering for machine learning pipelines. Berkeley, CA: Apress, 2024. Pp. 235–260. DOI: 10.1007/979-8-8688-0602-5_12.
Pagidi R.K., Kolli R., Mokkapati C. et al. Enhancing ETL performance using delta lake in data analytics solutions. Universal Research Reports. 2022. Vol. 9. No. 4. Pp. 473–495. DOI: 10.36676/urr.v9.i4.1381.
Patel P. Cross-functional data modeling and data mart design for sales intelligence. International Journal of Science and Research Archive. 2025. Vol. 16. No. 3. Pp. 1383–1392. DOI: 10.30574/ijsra.2025.16.3.2518.
Sharma V. An enlightening assessment of data mart exploration in promptly mounting data warehousing consequence. IARJSET. 2021. Vol. 8. No. 5. Pp. 264–268. DOI: 10.17148/IARJSET.2021.8544.
Tourouta E., Gorodnichev M., Polyantseva K., Moseva M. Providing fault tolerance of cluster computing systems based on fault-tolerant dynamic computation planning. Springer, 2022. Pp. 143–150. (Lecture Notes in Information Systems and Organisation) DOI: 10.1007/978-3-030-94252-6_12.
Veeramachaneni J. Designing self-healing ETL pipelines with airflow and databricks. International Journal of Science and Research Archive. 2025. Vol. 17. No. 3. Pp. 1037–1043. DOI: 10.30574/ijsra.2025.17.3.3114.
Wang F.-Y., Miao Q., Li X. et al. Transportation 5.0: The DAO to safe, secure, and sustainable intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems. 2023. Vol. 24. No. 10. Pp. 10262–10278. DOI: 10.1109/TITS.2023.3305380.
Youseff S., Farinu H., Barnty B. et al. Performance optimization and cost-efficiency in serverless ETL execution models. International Journal of Computer Engineering and Technology. 2026. Vol. 17. No. 1. Pp. 45–58. DOI: 10.34218/IJCET_17_01_005.
Zhu L., Yu F.R., Wang Y. et al. Big data analytics in intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems. 2019. Vol. 20. No. 1. Pp. 383–398. DOI: 10.1109/TITS.2018.2815678.
Polyantseva K.A., Egorova K.O. Automation of big data processing processes using DevOps. Economics and Quality of Communication Systems. 2025. No. 4 (38). Pp. 107–118. (In Rus.)
Beese J., Aier S., Haki K., Winter R. The impact of enterprise architecture management on information systems architecture complexity. European Journal of Information Systems. 2023. Vol. 32. No. 6. Pp. 1070–1090. DOI: 10.1080/0960085X.2022.2103045.
Dibouliya A. Self-serving data marts orchestrated by AutoML-Governed pipelines. International Journal of Environmental Sciences. 2025. Pp. 65–82. DOI: 10.64252/p9cqj306.
Gong T., Zhu L., Yu F.R., Tang T. Edge intelligence in intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems. 2023. Vol. 24. No. 9. Pp. 8919–8944. DOI: 10.1109/TITS.2023.3275741.
Haque M.A., Ahmad S., Alanazi S., John A. IoT-based data analysis and security for intelligent transportation system in smart cities. In: Synergies in Data Analytics and Cyber Security. Proceedings of the International Conference (DACS 2024) / D. Puthal, B.K. Panigrahi, N. Ray, Z. Ding (eds.). Singapore: Springer, 2026. Pp. 62–75. DOI: 10.1007/978-981-95-2680-2_62.
Narayanan P.K. Orchestrating data engineering pipelines using apache airflow. In: Data engineering for machine learning pipelines. Berkeley, CA: Apress, 2024. Pp. 235–260. DOI: 10.1007/979-8-8688-0602-5_12.
Pagidi R.K., Kolli R., Mokkapati C. et al. Enhancing ETL performance using delta lake in data analytics solutions. Universal Research Reports. 2022. Vol. 9. No. 4. Pp. 473–495. DOI: 10.36676/urr.v9.i4.1381.
Patel P. Cross-functional data modeling and data mart design for sales intelligence. International Journal of Science and Research Archive. 2025. Vol. 16. No. 3. Pp. 1383–1392. DOI: 10.30574/ijsra.2025.16.3.2518.
Sharma V. An enlightening assessment of data mart exploration in promptly mounting data warehousing consequence. IARJSET. 2021. Vol. 8. No. 5. Pp. 264–268. DOI: 10.17148/IARJSET.2021.8544.
Tourouta E., Gorodnichev M., Polyantseva K., Moseva M. Providing fault tolerance of cluster computing systems based on fault-tolerant dynamic computation planning. Springer, 2022. Pp. 143–150. (Lecture Notes in Information Systems and Organisation) DOI: 10.1007/978-3-030-94252-6_12.
Veeramachaneni J. Designing self-healing ETL pipelines with airflow and databricks. International Journal of Science and Research Archive. 2025. Vol. 17. No. 3. Pp. 1037–1043. DOI: 10.30574/ijsra.2025.17.3.3114.
Wang F.-Y., Miao Q., Li X. et al. Transportation 5.0: The DAO to safe, secure, and sustainable intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems. 2023. Vol. 24. No. 10. Pp. 10262–10278. DOI: 10.1109/TITS.2023.3305380.
Youseff S., Farinu H., Barnty B. et al. Performance optimization and cost-efficiency in serverless ETL execution models. International Journal of Computer Engineering and Technology. 2026. Vol. 17. No. 1. Pp. 45–58. DOI: 10.34218/IJCET_17_01_005.
Zhu L., Yu F.R., Wang Y. et al. Big data analytics in intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems. 2019. Vol. 20. No. 1. Pp. 383–398. DOI: 10.1109/TITS.2018.2815678.
Keywords:
data mart, intelligent transport systems, algorithm management, computational rules, ETL processes, data warehouses, dynamic query generation, architecture of information systems.
