Collision Resolution Hash Tables: A Comparative Performance Study Using Synthetic Data in C++

1. Wang, Q. (2025). The Bathroom Model: A realistic approach to hash table algorithm optimization arXiv. https://doi.org/10.48550/arXiv.2502.10977 2. Halkarnikar, P. P., Meshram, P. A., Joshi, S. S., Mahajan, D. A., & Pawar, V. (2024). Binary probing: A novel approach for efficient hash table operations. Proceedings of International Conference on Computational Inteligience ,153- 165.https://doi.org/10.1007/978-981-97-3526-6_13 3. Fatourou, P., Kallimanis, N. D., & Ropars, T. (2022). An efficient wait-free resizable hash table. arXiv. https://arxiv.org/abs/2204.09624 4. Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2022). Introduction to algorithms (4th ed.). MIT Press. 5. Hassan, F. A., Farah, N. I., & Haifaa, A. H. (2022). A review of hash function types and their applications. Wasit Journal of Computer and Mathematics Science, 3(1), 120–139. 6. Bender, M. A., Conway, A., Farach-Colton, M., Kuszmaul, W., & Tagliavini, G. (2021). Iceberg hashing: Optimizing many hash-table criteria at once. arXiv. https://arxiv.org/abs/2109.04548 7. Pandey, P., Bender, M. A., Conway, A., Farach-Colton, M., Kuszmaul, W., Tagliavini, G., & Johnson, R. (2022). IcebergHT: High performance PMEM hash tables through stability and low associativity. arXiv. https://arxiv.org/abs/2210.04068 8. Knuth, D. E. (2022). The art of computer programming, volume 3: Sorting and searching (2nd ed.). Addison-Wesley. 9. Hassan, F.A., Farah, N.I & Haifaa A.H (2022). A review of Hash function types and their applications. Wasist Journal of Computer and Mathematic Science, 3(1), 120-139. 10. Ahmed, D.Y., Saleh, A., Mouussa, M.B., & Salisu, I.Y (2021). Collision resolution techniques in hash table: A review. International Journal of Advance Computer Science and Application, 12(9), 120-139. 11. Qin, C., Zhang, L., Yang., Y., & Lu, C (2022). Adaptive and dynamic multi-resolution hashing for pairwise summations. Proceedings of the 39thInternational Conference on Machine Learning,162:18639-18658. https://proceedings.mir.press/v162/qin22a.html 12. Lu, Y., Shen, M., Wang, H., Wang, X., Van Rechem, C., Fu, T., & Wei, W. (2023), Machine learning for synthetic data generation: A review. arXiv. https://doi.org/10.48550/arXiv.2302.04062 13. Álvarez, Á., & Vaz, B. (2022). Survey on synthetic data generation, evaluation methods and GANs. Mathematics, 10(15), 2733. https://doi.org/10.3390/math10152733 14. Mitzenmacher, M.,&Upfai,E (2017).Probability and Computing: Randomization and probalististic techniques in algorithms and Data analysis (2nd ed.). 15. Nimbe, Peter, Samuel Ofori Frimpong, & Michael Opoku (2014).” An efficient strategy for collision resolution in hash tables,” International Journal of Computer Applications 99(10), 35-41 16. Bello, S.?A., Mukhtar, A.?L., Gezawa, A.?S., Garba, A., & Ado, A. (2014). Comparative analysis of linear probing, quadratic probing, and double hashing techniques for resolving collision in a hash table. International Journal of Scientific & Engineering Research, 5(4), 685–686.