JCEEES
Journal of Computer & Electrical and Electronics Engineering Sciences

JCEEES aims to publish original articles covering the theoretical foundations of major computer, electronic and electrical engineering sciences, as well as academic, commercial and educational aspects that propose new ideas for the application and design of artificial intelligence, software and information systems. In addition to wide-ranging regular topics, JCEEES also makes it a principle to include special topics covering specific topics in all areas of interest mainly in computational medicine, artificial intelligence, computer science, and electrical & electronic engineering science.

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Original Article
Performance comparison of compression algorithms for log data in compliance with Turkish Law No. 5651
Abstract
Aims: The aim of this study is to evaluate the performance of various lossless compression algorithms for storing log data in compliance with Turkish Law No. 5651.
Methods: In the study, compression and decompression processes were performed on 100 MB and 1 GB of FortiGate log data that was generated synthetically from sample data using the Zstandard, Brotli, GZIP, LZ4, and Snappy algorithms. The algorithms were evaluated based on the amount of data compressed, compression ratio, time, speed, decompression time, and speed.
Results: The experimental studies reveal that the Zstd algorithm provides a balanced performance between compression ratio and speed. While the Brotli algorithm provides the highest compression ratio, it has the longest compression time and the slowest compression speed. The LZ4 and Snappy algorithms, on the other hand, have provided the best results in terms of compression speed and time, but their compression rates have lagged behind those of other algorithms.
Conclusion: The study results show that compression algorithms reveal significant differences in terms of performance and efficiency in large-scale log systems. The selection of the appropriate algorithm should be determined based on criteria such as the system's speed, storage requirements, and processing time. The findings obtained reveal that compression strategies play a critical role in fulfilling legal log retention obligations.

Keywords : Turkish Law No. 5651, big data, data storage, lossless compression, firewall logs

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1. Abdulazeez, F. A., Ahmed, I. T., & Hammad, B. T. (2024). Examining the performance of various pretrained convolutional neural network models in malware detection. Appl Sci, 14(6), 2614. doi:10.3390/app140 62614
2. Ahmed, M. R., Khatun, M. A., Ali, M. A., & Sundaraj, K. (2018). A literature review on NoSQL database for big data processing. Int J Eng Technol, 7(2), 902-906. doi:10.14419/ijet.v7i2.12113
3. Almomani, I., Alkhayer, A., & El-Shafai, W. (2022). An automated vision-based deep learning model for efficient detection of android malware attacks. IEEE Access, 10, 2700-2720. doi:10.1109/ACCESS.2022.3140341
4. Baidoo, P. K. (2023). Comparative analysis of the compression of text data using Huffman, arithmetic, run-length, and Lempel Ziv Welch coding algorithms. J Adv Mathemat Comput Sci, 38(9), 144-156. doi:10. 9734/jamcs/2023/v38i91812
5. Btoush, M. H., & Dawahdeh, Z. E. (2018). A complexity analysis and entropy for different data compression algorithms on text files. J Comput Communicat, 6(1), 301. doi:10.4236/jcc.2018.61029
6. Butts-Wilmsmeyer, C. J., Rapp, S., & Guthrie, B. (2020). The technological advancements that enabled the age of big data in the environmental sciences: a history and future directions. Curr Opin Environment Sci Health, 18, 63-69. doi:10.1016/j.coesh.2020.07.006
7. Chen, Y., Zhang, F., Hong, Y., et al. (2022). Taming the big data monster: managing petabytes of data with multi-model databases. Proceedings of the 2022 IEEE 34th International Symposium on Computer Architecture and High Performance Computing (pp. 283-292). IEEE.
8. de Oliveira, V. F., Pessoa, M. A. O., Junqueira, F., & Miyagi, P. E. (2021). SQL and NoSQL databases in the context of Industry 4.0. Machines, 10(1), 20. doi:10.3390/machines10010020
9. Facebook. (2025a). Zstandard (Zstd) [Computer software]. GitHub. Retrieved June 20, 2025, from https://github.com/facebook/zstd
10. Facebook. (2025b). Zstandard API manual. Retrieved June 20, 2025, from https://facebook.github.io/zstd/doc/api_manual_latest.html#Chapter1
11. Google. (2025a). Brotli compression tool manual page (brotli.1). Retrieved June 20, 2025, from https://github.com/google/brotli/blob/master/docs/brotli.1
12. Google. (2025b). Snappy - a fast compressor/decompressor [computer software]. GitHub. Retrieved June 20, 2025, from https://github.com/google/snappy
13. GNU Project. (2025). Gzip [Software]. GNU Operating System. Retrieved June 20, 2025, from https://www.gnu.org/software/gzip/manual/gzip.html
14. Gupta, A., & Nigam, S. (2021). A review on different types of lossless data compression techniques. Int J Sci Res Comput Sci, 7(1), 50-56. doi: 10.32628/cseit217113
15. IETF. (2016). Brotli compressed data format (RFC 7932). https://datatracker.ietf.org/doc/html/rfc7932
16. Jaranilla, C., & Choi, J. (2023). Requirements and trade-offs of compression techniques in key-value stores: a survey. Electronics, 12(20), 4280. doi:10.3390/electronics12204280
17. Jing, W., Tong, D., Chen, G., Zhao, C., & Zhu, L. (2018). An optimized method of HDFS for massive small files storage. Comput Sci Informat Syst, 15(3), 533-548. doi:10.2298/CSIS170926034J
18. Kodituwakku, S. R., & Amarasinghe, U. S. (2010). Comparison of lossless data compression algorithms for text data. Indian J Comput Sci Eng, 1(4), 416-425.
19. Republic of Turkiye. (2007). Law No. 5651: Regulation on the procedures and principles regarding the regulation of publications made on the internet environment. https://www.mevzuat.gov.tr/mevzuat?MevzuatNo=5651&MevzuatTur=1&MevzuatTertip=5
20. Li, X., Zhang, H., Le, V. H., & Chen, P. (2024, February). Logshrink: Effective log compression by leveraging commonality and variability of log data. InProceedings of the 46th IEEE/ACM International Conference on Software Engineering (pp. 1-12).
21. Liu, Q., Xu, Y., & Li, Z. (2019, February). DecMac: a deep context model for high efficiency arithmetic coding. In 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (pp. 438-443). IEEE.
22. Moharm, K. (2019). State of the art in big data applications in microgrid: A review. Adv Eng Informat, 42, 100945. doi:10.1016/j.aei.2019.100945
23. Prayogo, A. I., Nugraha, A., Kurniawan, J. C., Kusuma, E. J., & Wibowo, A. (2024). Enhancing least significant bit steganography image fidelity using Brotli compression. Sinkron J Res Informat Technol, 8(1), 285-295. doi:10.33395/sinkron.v9i1.13186
24. Raban, D. R., & Gordon, A. (2020). The evolution of data science and big data research: a bibliometric analysis. Scientometrics, 122(3), 1563-1581. doi:10.1007/s11192-019-03332-4
25. Rashid, A., & Chaturvedi, A. (2019). Cloud computing characteristics and services: a brief review. Int J Comput Sci Eng, 7(2), 421-426. doi:10. 26438/ijcse/v7i2.421426
26. Security, R. (2023). Detecting attacks using Fortigate firewall logs with log samples [Blog post]. Medium. Retrieved June 20, 2025, from https://readsecurity.medium.com/detecting-attacks-using-fortigate-firewall-logs-with-log-samples-ad0e7ee57903
27. Senthil, S., & Robert, L. (2011). Text compression algorithms: a comparative study. J Commun Technol, 2(4), 444-451. doi:10.21917/ijct. 2011.0062
28. Syed, Z., & Soomro, T. (2018). Compression algorithms: Brotli, Gzip and Zopfli perspective. Indian J Sci Technol, 11(45), 1-4. doi:10.17485/ijst/2018/v11i45/117921
29. Theodorakopoulos, L., Theodoropoulou, A., & Stamatiou, Y. (2024). A state-of-the-art review in big data management engineering: real-life case studies, challenges, and future research directions. Eng, 5(3), 1266-1297. doi:10.3390/eng5030071
30. UNCTAD. (2025a). Summary of adoption of e-commerce legislation worldwide. United Nations Conference on Trade and Development. Retrieved June 20, 2025, from https://unctad.org/topic/ecommerce-and-digital-economy/ecommerce-law-reform/summary-adoption-e-commerce-legislation-worldwide
31. UNCTAD. (2025b). Cyberlaw tracker - Turkiye. United Nations Conference on Trade and Development. Retrieved June 20, 2025, from https://unctad.org/page/cyberlaw-tracker-country-detail?country=tr
32. Wang, S., Jiang, X., & Khaskheli, M. B. (2024). The role of technology in the digital economy’s sustainable development of Hainan Free Trade Port and genetic testing: cloud computing and digital law. Sustainability, 16(14), 6025. doi:10.3390/su16146025
33. Yang, P., Xiong, N., & Ren, J. (2020). Data security and privacy protection for cloud storage: a survey. IEEE Access, 8, 131723-131740. doi:10.1109/ACCESS.2020.3009876 </ol> <p>
Article available in :
Volume 3, Issue 2, 2025
Page : 54-60
https://doi.org/10.51271/JCEEES-0029
Article Information
Received : 01 Ağu 2025
Accepted : 09 Eyl 2025
Published : 03 Eki 2025
Corresponding Author :

Melih Karahan: Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Kırıkkale University, Kırıkkale, Turkiye

[email protected]
Citation : Karahan, M., Erdal E., & Ergüzen, A. (2025). Performance comparison of compression algorithms for log data in compliance with Turkish Law No. 5651. J Comp Electr Electron Eng Sci, 3(2), 54-60.

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