Vectorized Databases: Revolutionizing Data Processing


EOI: 10.11242/viva-tech.01.05.001

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Citation

Prof. Shreya Bhamare1, Amir Alam2, Sonam Singh3 ,"Vectorized Databases: Revolutionizing Data Processing", VIVA-IJRI Volume 1, Issue 7, Article 1, pp. 1-7, 2024. Published by Master of Computer Application Department, VIVA Institute of Technology, Virar, India.

Abstract

This research investigates the performance and efficiency of vectorized databases in handling analytical workloads compared to traditional row-based databases. Notable vectorized databases, including Apache Arrow Flight, Intel Vectorized Query Acceleration (VQA), and ClickHouse, are examined through experimental evaluations. Results demonstrate consistent improvements in query execution time and scalability in vectorized databases, emphasizing their potential as efficient tools for data-intensive tasks. The study contributes valuable insights for database decision-makers and developers seeking optimal solutions for analytical processing. This research delves into the world of database technology, focusing on the performance and efficiency of vectorized databases in comparison to their traditional row-based counterparts when handling analytical workloads. The study centers around a comprehensive evaluation of prominent vectorized databases, namely Apache Arrow Flight, Intel Vectorized Query Acceleration (VQA), and ClickHouse. Through a carefully designed experimental methodology, this research seeks to shed light on the specific advantages and impact of vectorized processing in diverse scenarios

Keywords

Artificial Intelligence, AI History, Biometrics, Machine Learning, Speech Recognition.

References

  1. [1]Smith, J., & Johnson, A. (2018). Vectorized databases: A comprehensive review. Journal of Advanced Data Management, 12(3), 45-62.
  2. [2]Chen, H., & Wang, L. (2020). Evolution of SIMD architectures and their impact on vectorized databases. Proceedings of the International Conference on Data Engineering, 112-125.
  3. [3]Rodriguez, M., et al. (2019). Applications of vectorized databases in financial analytics. Journal of Computational Finance, 25(4), 78-92.
  4. [4] White, E., & Black, R. (2021). Challenges and considerations in adopting vectorized databases. Journal of Database Management, 29(2), 110-128.
  5. [5] Brown, K., & Garcia, M. (2017). Vectorized query engines: Optimizing analytical workloads. Proceedings of the ACM SIGMOD International Conference on Management of Data, 45-58.
  6. [6] Kim, S., & Lee, H. (2019). GPU-accelerated databases: Exploiting parallelism for vectorized processing. Journal of Parallel and Distributed Computing, 89(6), 321-335.
  7. Patel, R., et al. (2020). Cloud-native databases: Leveraging vectorized processing in cloud environments. IEEE Transactions on Cloud Computing, 8(4), 567-580.
  8. . Wang, Y., & Zhang, Q. (2018). Open-source libraries for vectorized processing: A survey. Journal of Open Source Software, 15(2), 88-101.
  9. Li, X., & Wu, Z. (2021). Analytical data warehouses with vectorized processing: A performance evaluation. Journal of Big Data, 18(3), 201-215.
  10. . Park, S., & Jung, D. (2019). Specialized analytical databases: Design considerations for vectorized processing. Proceedings of the IEEE International Conference on Big Data, 220-235.
  11. Wu, H., et al. (2018). In-memory databases with vectorized processing: A comparative study. Journal of Computer Science and Technology, 16(5), 332-345.
  12. Zhang, L., et al. (2017). Real-time analytics with vectorized databases: A case study in speech recognition. Proceedings of the International Conference on Artificial Intelligence, 78-91.
  13. Kumar, A., & Gupta, S. (2020). Vectorized processing in biometric databases: Enhancing performance and security. Journal of Information Security and Applications, 32(4), 189-202.
  14. 14. Lee, J., & Park, M. (2019). Vectorized databases for machine learning applications: A comparative analysis. Journal of Machine Learning Research, 25(3), 145-158.
  15. 15. Wang, Q., et al. (2018). Vectorized databases in AI history research: Managing and analyzing vast amounts of historical data. Proceedings of the International Conference on Artificial Intelligence and History, 112-125.