Five-Tier BI Architecture with Tuned Decision Trees For E-Commerce Prediction

  • Authors

    • Thiruneelakandan A Research Scholar, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India
    • Umamageswari A Associate Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India https://orcid.org/0000-0001-9750-2127
    https://doi.org/10.14419/2drax454

    Received date: July 20, 2025

    Accepted date: August 28, 2025

    Published date: August 31, 2025

  • Five-Tier Service-Service Oriented BI Architecture, QoS-Aware Service Discovery, Tuned Decision Tree, Large Language Model Toolchain, Retrieval Augmented Generation

  • Abstract

    In recent times, remarkable performance has been shown by Large Language Models (LLMs) in a range of Natural Language Processing (NLP) such as questioning, responding, document production, and translating languages. In today's competitive business landscape, understanding consumer behaviour in online buying is crucial for the success of e-commerce platforms. The work proposes a novel Five-Tier Service-Oriented BI Architecture (FSOBIA) that leverages Advanced Tuned Decision Tree (ATDT) techniques for predicting online buying behaviour. The proposed FSOBIA offers e-commerce platforms a scalable and adaptable solution for gaining insights into consumer preferences and making informed business decisions. The goal of FSOBIA's design and implementation is to meet the needs of evolving users and provide quicker service. Experimental evaluations on real-world datasets in FSOBIA achieved over 95% prediction accuracy, outperforming traditional models: Decision Trees (82%) and XGBoost (91%), while offering better scalability and computational efficiency.

  • References

    1. Liu, W., & Deng, Y. (2024). QoS-aware resource allocation method for the internet of things using triplet and heterogeneous earliest finish time algorithms. Proceedings of the Indian National Science Academy, 90(1), 22-30. https://doi.org/10.1007/s43538-023-00215-4
    2. Choudhury, A., & Shamszare, H. (2023). Investigating the impact of user trust on the adoption and use of ChatGPT: survey analysis. Journal of Medical Internet Research, 25, e47184.
    3. Kalibatienė, D., Miliauskaitė, J., Slotkienė, A., & Gudas, S. (2023). On the development of the web service quality modelling space. Expert Sys-tems with Applications, 211, 118584. https://doi.org/10.1016/j.eswa.2022.118584
    4. Jain, V., & Kumar, B. (2022). QoS-aware task offloading in fog environment using multi-agent deep reinforcement learning. Journal of Network and Systems Management, 31(1), 7-7. https://doi.org/10.1007/s10922-022-09696-y
    5. Mohammed, A. M., Haytamy, S. S. A., & Omara, F. A. (2023). Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition. International Journal of Electrical and Computer Engineering (IJECE), 13(1), 638-650. https://doi.org/10.11591/ijece.v13i1.pp638-650
    6. Chen, Z., Bao, T., Qi, W., You, D., Liu, L., & Shen, L. (2024). Poisoning QoS-aware cloud API recommender system with generative adversarial network attack. Expert Systems with Applications, 238, 121630. https://doi.org/10.1016/j.eswa.2023.121630
    7. Saif, M. A. N., Niranjan, S. K., Murshed, B. A. H., Al-ariki, H. D. E., & Abdulwahab, H. M. (2022). Multi-agent QoS-aware autonomic resource provisioning framework for elastic BPM in containerized multi-cloud environment. Journal of Ambient Intelligence and Humanized Computing, 14, 12895–12920.
    8. Malla, P. A., & Sheikh, S. (2023). Analysis of QoS aware energy-efficient resource provisioning techniques in cloud computing. International Jour-nal of Communication Systems, 36(1), e5359. https://doi.org/10.1002/dac.5359
    9. Swathi, V. N. V. L. S., Kumar, G. S., & Vathsala, A. V. (2023). Cloud service selection system approach based on QoS model: A systematic re-view. International Journal on Recent and Innovation Trends in Computing and Communication.
    10. Shuling, Y. I. N., & Renping, Y. U. (2023). A QoS-aware resource allocation method for Internet of Things using ant colony optimization algo-rithm and tabu search. International Journal of Advanced Computer Science and Applications, 14(9). https://doi.org/10.14569/IJACSA.2023.0140997
    11. Liu, W. (2024). QoS-aware resource allocation method for the internet of things using triplet and heterogeneous earliest finish time algorithms. Pro-ceedings of the Indian National Science Academy, 90(1), 22–30. https://doi.org/10.1007/s43538-023-00215-4
    12. Purohit, L., Rathore, S. S., & Kumar, S. (2023). A QoS-aware clustering based multi-layer model for web service selection. IEEE Transactions on Services Computing, 16, 3141–3154.
    13. Cerquitelli, T., Meo, M., Curado, M., Skorin-Kapov, L., & Tsiropoulou, E. E. (2023). Machine learning empowered computer networks. Computer Networks, 230, 109807. https://doi.org/10.1016/j.comnet.2023.109807
    14. Li, J., Wu, H., He, Q., Zhao, Y., & Wang, X. (2024). Dynamic QoS prediction with intelligent route estimation via inverse reinforcement learning. IEEE Transactions on Services Computing, 17(2), 509–523. https://doi.org/10.1109/TSC.2023.3342481
    15. Merin, J. B., Banu, W. A., Akila, R., & Radhika, A. (2023). Semantic annotation based mechanism for web service discovery and recommendation. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 14, 169–185.
    16. Joshi, N., & Srivastava, S. (2023). QoS-aware task allocation and scheduling in cloud-fog-edge architecture with proactive migration strategy. SSRN. https://doi.org/10.2139/ssrn.4515500
    17. Avval, D. B., Heris, P. O., Navimipour, N. J., Mohammadi, B., & Yalçin, S. (2022). A new QoS-aware method for production scheduling in the industrial internet of things using elephant herding optimization algorithm. Cluster Computing, 26, 3611–3626.
    18. Russo, G. R., Ferrarelli, D., Pasquali, D., Cardellini, V., & Presti, F. L. (2024). QoS-aware offloading policies for serverless functions in the Cloud-to-Edge continuum. Future Generation Computer Systems, 156, 1–15. https://doi.org/10.1016/j.future.2024.02.019
    19. Taleb, T., Benzaïd, C., Addad, R. A., & Samdanis, K. (2023). AI/ML for beyond 5G systems: Concepts, technology enablers, and solutions. Com-puter Networks, 237, 110044. https://doi.org/10.1016/j.comnet.2023.110044
    20. Srikanth, G. U., & Geetha, R. (2023). Effectiveness review of the machine learning algorithms for scheduling in cloud environment. Archives of Computational Methods in Engineering, 30(6), 3769–3789. https://doi.org/10.1007/s11831-023-09921-0
    21. M. U. Hassan, A., Al-Awady, A. A., Ali, A., Iqbal, M. M., Akram, M., & Jamil, H. (2024). Smart resource allocation in mobile cloud next-generation network (NGN) orchestration with context-aware data and machine learning for the cost optimization of microservice applications. Sen-sors, 24(3). https://doi.org/10.3390/s24030865
    22. Jin, H., Jiang, C., & Lv, S. (2023). A hybrid whale optimization algorithm for quality of service-aware manufacturing cloud service composition. Symmetry, 16, 46.
    23. Ma, W., & Xu, H. (2023). Skyline-enhanced deep reinforcement learning approach for energy-efficient and QoS-guaranteed multi-cloud service composition. Applied Sciences, 13(11). https://doi.org/10.3390/app13116826
    24. Amiri, Z., Heidari, A., Darbandi, M., Yazdani, Y., Jafari Navimipour, N., Esmaeilpour, M., Sheykhi, F., & Unal, M. (2023). The personal health applications of machine learning techniques in the Internet of Behaviors. Sustainability, 15(16). https://doi.org/10.3390/su151612406
    25. Njima, C. B., Guégan, C. G., Gamha, Y., & Romdhane, L. B. (2024). Web service composition in mobile environment: A survey of techniques. IEEE Transactions on Services Computing, 17, 689–704.
    26. Thantharate, A., & Beard, C. (2022). ADAPTIVE6G: Adaptive resource management for network slicing architectures in current 5G and future 6G systems. Journal of Network and Systems Management, 31(1), 9-9. https://doi.org/10.1007/s10922-022-09693-1
    27. Ramesh, J. V. N., Khasim, S., Abbas, M., Shaik, K., Rahman, M. Z. U., & Elangovan, M. (2023). Cloud services user’s recommendation system using random iterative fuzzy-based trust computation and support vector regression. Mathematics, 11(10). https://doi.org/10.3390/math11102332
    28. Hazra, A., Rana, P., Adhikari, M., & Amgoth, T. (2023). Fog computing for next-generation Internet of Things: Fundamental, state-of-the-art and research challenges. Computer Science Review, 48, 100549. https://doi.org/10.1016/j.cosrev.2023.100549
    29. J., J., R., J., & K., R. K. (2024). A New Integrated Approach for Cloud Service Composition and Sharing Using a Hybrid Algorithm. Mathematical Problems in Engineering, 2024(1), 3136546. https://doi.org/10.1155/2024/3136546
    30. Jaafar, A. A., Jawawi, D. N. A., Isa, M. A., & Saadon, N. A. (2023). Service selection model based on user intention and context. Journal of King Saud University - Computer and Information Sciences, 35(4), 209–223. https://doi.org/10.1016/j.jksuci.2023.03.018
    31. Kumar, R., & Agrawal, N. (2023). Analysis of multi-dimensional Industrial IoT (IIoT) data in Edge–Fog–Cloud based architectural frameworks: A survey on current state and research challenges. Journal of Industrial Information Integration, 35, 100504. https://doi.org/10.1016/j.jii.2023.100504
    32. M. H. H. Hassan, M. U., Al-Awady, A. A., Ali, A., Iqbal, M. M., Akram, M., & Jamil, H. (2023). Applications of machine learning in mobile net-working. Journal of Smart Internet of Things, 2023(1), 23-35. https://doi.org/10.2478/jsiot-2023-0003
    33. Xie, L., Liu, J. & Wang, W. Predicting sales and cross-border e-commerce supply chain management using artificial neural networks and the Capu-chin search algorithm. Sci Rep 14, 13297 (2024). https://doi.org/10.1038/s41598-024-62368-6
    34. R. Esmeli and A. Gokce, "An Analysis of Consumer Purchase Behavior Following Cart Addition in E-Commerce Utilizing Explainable Artificial Intelligence," Journal of Theoretical and Applied Electronic Commerce Research, vol. 20(1), 28, 2025. https://doi.org/10.3390/jtaer20010028

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  • How to Cite

    A, T., & A, U. . (2025). Five-Tier BI Architecture with Tuned Decision Trees For E-Commerce Prediction. International Journal of Basic and Applied Sciences, 14(4), 795-801. https://doi.org/10.14419/2drax454