Good data structure selection plays an important role in utilizing algorithm performance in computer science. Generally, software developers manually select data structures based on problem requirements and their experience level. The complexity of real-time systems, which have large data content processing requirements, is increasing. A real-time system uses a large-scale dataset that requires large-scale data processing. With the increasing complexity of real-time systems, the manual selection of data structures is often inefficient. Machine learning-based automated decision-making processes and data structure selection have emerged as excellent approaches in computer science
This paper presents an analysis of machine learning techniques applied to real-time data structure selection. This explains how ML models can dynamically select optimal data structures based on input characteristics, workload patterns, and performance metrics. This review paper includes the complete system architecture, feature engineering, model selection, empirical evaluation, and reproducibility considerations. This review paper also examines real-world case studies, compares different ML methods with traditional techniques, and discusses key challenges such as computational overhead, data scarcity, generalization, and interpretability. Finally, the paper outlines future directions, including explainable AI, compiler integration, edge computing optimization, and online learning systems.
Keywords
Machine Learning, Data Structures, Real-Time Systems, Adaptive Systems, Optimization
Conclusion
Machine learning-based data structure selection represents a major advancement over traditional approaches. It enables intelligent, adaptive, and real-time decision-making, leading to improved performance and scalability.
Despite challenges such as computational overhead, data scarcity, and interpretability, ongoing research is addressing these issues. Future developments in explainable AI, system integration, and adaptive learning will further enhance this field and enable widespread adoption.
References
[1] A. Patel et al., “Analysis of Data Structures using Machine Learning,” International Journal of Engineering Research & Technology (IJERT), vol. 9, no. 7, 2020. [2] S. Mishra et al., “Optimization of Data Structures in Machine Learning,” International Journal of Computer Sciences and Engineering (IJCSE), 2021. [3] D. Kumar et al., “Real-Time Data Processing using Machine Learning,” IJERT, vol. 10, no. 6, 2021. [4] P. Singh et al., “Machine Learning for Real-Time Systems,” International Research Journal of Engineering and Technology (IRJET), vol. 9, no. 3, 2022. [5] P. Singh et al., “Feature Selection Techniques in Machine Learning,” IJERT, vol. 8, no. 4, 2019. [6] N. Krishnaveni and V. Radha, “Feature Selection Algorithms for Data Mining Classification: A Survey,” Indian Journal of Science and Technology, 2019. [7] K. Nongmeikapam and S. Bandyopadhyay, “Genetic Algorithm for Feature Selection in Natural Language Processing,” 2011. [8] D. Agrawal and A. Dubey, “A Survey of Machine Learning Algorithms for Big Data Analytics,” IJSRSET, 2019. [9] R. Kaur et al., “Comparative Study of Machine Learning Algorithms,” IJERT, vol. 7, no. 6, 2018. [10] S. Kumar et al., “Machine Learning Techniques: A Survey,” IJERT, vol. 10, no. 5, 2021.
How to Cite This Paper
Rina Sharma (2026). A Review Paper on Machine Learning Applications in Real-Time Data Structure Selection. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
