Design and implementation of distributed systems for big data processing using artificial intelligence algorithms
Subject Areas : New technologies in distributed systems and algorithmic computing
1 - Department of Mathematics Education, Farhangian University, Tehran, Iran
Keywords: Distributed Systems, Big Data processing, Artificial Intelligence Algorithms, Performance Improvement, Systems Efficiency,
Abstract :
[1] Aminizadeh, S., Heidari, A., Toumaj, S., Darbandi, M., Navimipour, N. J., Rezaei, M., ... & Unal, M. (2023). The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things. Computer methods and programs in biomedicine, 107745.
[2] Al-Jumaili, A. H. A., Muniyandi, R. C., Hasan, M. K., Paw, J. K. S., & Singh, M. J. (2023). Big data analytics using cloud computing based frameworks for power management systems: Status, constraints, and future recommendations. Sensors, 23(6), 2952.
[3] Khang, A., Gupta, S. K., Rani, S., & Karras, D. A. (Eds.). (2023). Smart Cities: IoT Technologies, big data solutions, cloud platforms, and cybersecurity techniques. CRC Press.
[4] Manikandan, N., Tadiboina, S. N., Khan, M. S., Singh, R., & Gupta, K. K. (2023, May). Automation of Smart Home for the Wellbeing of Elders Using Empirical Big Data Analysis. In 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 1164-1168). IEEE.
[6] Hong, S. C. T.-L., S. D'Oca, D. Yan, S. P. (2016). Advances in research and applications of energy-related occupant behavior in buildings. Electronic Library, 116, 694-704.
[7] M. Denil, L. Bazzani, H. Larochelle, and N. de Freitas. Learning where to attend with deep architectures for image tracking. Neural computation, 24(8):2151–2184, 2012
[8] Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16-28.
[9] Chunduri, R. K., & Cherukuri, A. K. (2021). Scalable algorithm for generation of attribute implication base using FP-growth and spark. Soft Computing, 1-22.
[10] D’Oca, S., Chen, C. F., Hong, T., & Belafi, Z. . (2017). Synthesizing building physics with social psychology: An interdisciplinary framework for context and occupant behavior in office buildings. Energy research & social science, 34, 240-251.
[11] Fan, S. X., F. (2018). Mining big building operational data for improving building energy efficiency: a case study. Build. Serv. Eng. Res. Technol, 39, 117-128.
[12] Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.
[13] Laender, A. H., Ribeiro-Neto, B. A., Da Silva, A. S., & Teixeira, J. S. (2002). A brief survey of web data extraction tools. ACM Sigmod Record, 31(2), 84-93.
Loshin, D. (2013). Business Intelligence (Second Edition):
[14] Morgan Kaufmann Mirmozaffari, M., Boskabadi, A., Azeem, G., Massah, R., Boskabadi, E., Dolatsara, H. A., & Liravian, A. (2020). Machine learning clustering algorithms based on the DEA optimization approach for banking system in developing countries. European Journal of Engineering and Technology Research, 5(6), 651-658.
[15] Nabilah, A., Devita, H. P., Van Halen, Y., & Jurizat, A. (2021). Energy Efficiency in Church Building Based on Sefaira Energy Use Intensity Standard. Paper presented at the IOP Conference Series: Earth and Environmental Science.
[16] Poelmans, J., Dedene, G., Verheyden, G., Van der Mussele, H., Viaene, S., & Peters, E. (2010). Combining business process and data discovery techniques for analyzing and improving integrated care pathways. Paper presented at the Industrial Conference on Data Mining.
[17] Qamar Shahbaz Ul Haq. (2016). Data Mapping for Data Warehouse Design: Morgan Kaufmann
[18] Qiu, F. F., Z. Li, G. Yang, P. Xu, Z. Li. (2019). Data mining based framework to identify rule based operation strategies for buildings with power metering system. Build. Simul, 12, 195-205.
[14] Salvador García, J. L., Francisco Herrera. (2014). Data Preprocessing in Data Mining: Springe
[15] Sherman, R. (2015). Business Intelligence Guidebook: Morgan Kaufmann.
Zhang. (2015). A New Data Transformation Method and Its Empirical Research Based on Inverted Cycloidal Kinetic Model. Procedia Computer Science, 55, 485-492.
[16] D. Held, S. Thrun, and S. Savarese. Learning to track at 100 fps with deep regression networks. arXiv preprint arXiv:1604.01802, 2016.
[17] Vatter, J., Mayer, R., & Jacobsen, H. A. (2023). The evolution of distributed systems for graph neural networks and their origin in graph processing and deep learning: A survey. ACM Computing Surveys, 56(1), 1-37.
[18] S. Hong, T. You, S. Kwak, and B. Han. Online tracking by learning discriminative saliency map with convolutional neural network. arXiv preprint arXiv:1502.06796, 2015.
[19] J. Johnson, A. Alahi, and L. Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. arXiv preprint arXiv:1603.08155, 2016.
[20] S. E. Kahou, V. Michalski, and R. Memisevic. Ratm: Recurrent attentive tracking model. arXiv preprint arXiv:1510.08660, 2015.
[21] M. Kristan, J. Matas, A. Leonardis, M. Felsberg, L. Cehovin, G. Fernandez, T. Vojir, G. Hager, G. Nebehay, and R. Pflugfelder. The visual object tracking vot2015 challenge results. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 1–23, 2015.
[22] Olaniyi, O., Okunleye, O. J., & Olabanji, S. O. (2023). Advancing data-driven decision-making in smart cities through big data analytics: A comprehensive review of existing literature. Current Journal of Applied Science and Technology, 42(25), 10-18.
[23] Himeur, Y., Elnour, M., Fadli, F., Meskin, N., Petri, I., Rezgui, Y., ... & Amira, A. (2023). AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives. Artificial Intelligence Review, 56(6), 4929-5021.