The processing of big data is an exceedingly urgent challenge in the functioning of modern information systems. The latest information technologies must be employed to collect, store, and analyze vast amounts of information. Intelligent data processing systems were implemented in numerous fields, particularly in the industry. Smart industrial systems also utilize data from various devices, enabling automated management processes and network component analysis. A prime example of an intelligent industrial system is the smart grid, which efficiently distributes electricity to users by considering demand, network parameters, load, etc. Processing large amounts of information necessitates the use of machine learning methods and mathematical data analysis. Matrix factorization serves as an exemplary technique for transforming information into a more convenient form for further processing, establishing relationships between elements, and optimizing outcomes. In particular, the SVD (Singular Value Decomposition) and Funk-SVD algorithms are employed to address big data processing challenges, and they were discussed in this work. The key features of processing large data volumes in industrial smart grid systems were analyzed in the paper. The advantages of distributed computing for more efficient information analysis were identified. The recommendation algorithms that enable faster and more accurate processing of extensive data were explored in the study. Specifically, the SVD and Funk-SVD algorithms, used in recommendation systems for large data processing, were examined. A method of distributed matrix factorization to provide recommendations to smart grid system users was proposed in the paper. This approach involves the exchange of public data between devices and the local processing of private data. The advantages of this distributed model include flexibility in adjusting parameters, improved calculation accuracy through result exchange between nodes, high data processing speed, and scalability were identified. The conclusion that the proposed method can be effectively used in recommendation systems within the smart grid context, enhancing automated management processes and resource distribution was exclaimed.
[1] M. Li, H. Wang and J. Li, "Mining conditional functional dependency rules on big data," in Big Data Mining and Analytics, vol. 3, no. 1, pp. 68-84, March 2020, doi: 10.26599/BDMA.2019.9020019.
[2] A. Cuzzocrea, "Big Data Lakes: Models, Frameworks, and Techniques," 2021 IEEE International Conference on Big Data and Smart Computing (BigComp), Jeju Island, Korea (South), 2021, pp. 1-4, doi: 10.1109/BigComp51126.2021.00010.
[3] F. Peng, H. Wang, L. Zhuang, M. Wang and C. Yang, "Methods of enterprise electronic file content information mining under big data environment," 2020 International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), Bangkok, Thailand, 2020, pp. 5-8, doi: 10.1109/ICBASE51474.2020.00008.
[4] M. P. Maharani, P. Tobianto Daely, J. M. Lee and D. -S. Kim, "Attack Detection in Fog Layer for IIoT Based on Machine Learning Approach," 2020 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea (South), 2020, pp. 1880-1882, doi: 10.1109/ICTC49870.2020.9289380.
[5] M. Klymash, O. Hordiichuk-Bublivska, M. Kyryk, L. Fabri and H. Kopets, "Big Data Analysis in IIoT Systems Using the Federated Machine Learning Method," 2022 IEEE 16th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), Lviv-Slavske, Ukraine, 2022, pp. 248-252, doi: 10.1109/TCSET55632.2022.9766908.
[6] S. K. Kishore, G. Vasukidevi, E. P. C. Prasad, T. R. Patnala, V. P. Reddy and P. B. Chanda, "A Real- Time Machine learning based cloud computing Architecture for Smart Manufacturing," 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India, 2022, pp. 562-565, doi: 10.1109/ICAAIC53929.2022.9792860.
[7] M. D. Choudhry, J. S, B. Rose and S. M. P, "Machine Learning Frameworks for Industrial Internet of Things (IIoT): A Comprehensive Analysis," 2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), Trichy, India, 2022, pp. 1-6, doi: 10.1109/ICEEICT53079.2022.9768630.
[8] B. Walek and P. Fajmon, "A Recommender System for Recommending Suitable Products in E-shop Using Explanations," 2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC), Cairo, Egypt, 2022, pp. 16-20, doi: 10.1109/AIRC56195.2022.9836983.
[9] R. Sharma, S. Rani and S. Tanwar, "Machine Learning Algorithms for building Recommender Systems," 2019 International Conference on Intelligent Computing and Control Systems (ICCS), Madurai, India, 2019, pp. 785-790, doi: 10.1109/ICCS45141.2019.9065538.
[10] B. P. Kulkarni, S. Sai Krishna, K. Meenakshi, P. Kora and K. Swaraja, "Performance Analysis of Optimization Algorithms GA, PSO, and ABC based on DWT-SVD watermarking in OpenCV Python Environment," 2020 International Conference for Emerging Technology (INCET), Belgaum, India, 2020, pp. 1-5, doi: 10.1109/INCET49848.2020.9154134.
[11] R. K. Patel, A. Kumari, S. Tanwar, W. -C. Hong and R. Sharma, "AI-Empowered Recommender System for Renewable Energy Harvesting in Smart Grid System," in IEEE Access, vol. 10, pp. 24316-24326, 2022, doi: 10.1109/ACCESS.2022.3152528.
[12] Yu Jun, Olena Hordiichuk-Bublivska, Yan Lingyu, Marian Kyryk, Mykola Beshley, Hu Jiwei, “Big Data Аnalysis in Smart Grid Systems”, 18th IMEKO TC10 Conference “Measurement for Diagnostics, Optimisation and Control to Support Sustainability and Resilience” Warsaw, Poland, September 26–27, 2022
[13] N. Zhi-hong and Z. Fei, "Research on Semi-supervised Recommendation Algorithm Based on Hybrid Model," 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Taiyuan, China, 2020, pp. 344-348, doi: 10.1109/MLBDBI51377.2020.00073.
[14] Y. Jaradat, M. Masoud, I. Jannoud, A. Manasrah and M. Alia, "A Tutorial on Singular Value Decomposition with Applications on Image Compression and Dimensionality Reduction," 2021 International Conference on Information Technology (ICIT), Amman, Jordan, 2021, pp. 769-772, doi: 10.1109/ICIT52682.2021.9491732.
[15] M. Klymash, M. Kyryk, Y. Pyrih, O. Hordiichuk-Bublivska and T. Andrukhiv, "Model of Large Sparse Datasets Processing Efficiency in IIOT," 2023 17th International Conference on the Experience of Designing and Application of CAD Systems (CADSM), Jaroslaw, Poland, 2023, pp. 45-49, doi: 10.1109/CADSM58174.2023.10076508.
[16] G. Xin, J. Qin, X. Song and J. Zheng, "Dual Auto-Encoder Based Rating Prediction Recommendation Algorithm," in IEEE Access, vol. 10, pp. 97289-97297, 2022, doi: 10.1109/ACCESS.2022.3205610.