NoSQL Databases and Master Data Management: Revolutionizing Data Storage and Retrieval
Abstract
This research paper delves into the transformative impact of NoSQL databases on Master Data Management (MDM), highlighting their role in revolutionizing data storage and retrieval processes. As organizations grapple with increasing volumes of diverse and unstructured data, traditional relational databases face limitations in scalability and flexibility. The paper explores how NoSQL databases address these challenges by providing agile and scalable solutions for managing master data. The abstract will further delve into specific NoSQL database technologies, examining their unique features and advantages in the context of MDM. It will discuss real-world case studies and implementations where the integration of NoSQL databases has led to enhanced efficiency, improved data quality, and streamlined data retrieval processes within the MDM framework. Additionally, potential challenges and considerations associated with adopting NoSQL databases for MDM will be analyzed, offering insights for organizations navigating the evolving landscape of data management technologies.
References
Han, J., Haihong, E., & Le, G. (2011). Survey on NoSQL database. In Proceedings of 2011 6th Joint International Conference on Information Sciences (pp. 185-188). IEEE.
Wang, R. Y., & Zhang, C. (2012). Data quality issues in implementing an MDM solution. In Data Governance (pp. 115-133). Springer.
Batini, C., Cappiello, C., Francalanci, C., & Maurino, A. (2015). Methodologies for data quality assessment and improvement. ACM Computing Surveys (CSUR), 47(1), 1-52.
Sadalage, P. J., & Fowler, M. (2012). NoSQL distilled: A brief guide to the emerging world of polyglot persistence. Addison-Wesley.
Patel, N., & Patel, N. (2016). NoSQL database: New era of databases for big data analytics - classification, characteristics and comparison. In 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) (pp. 2074-2079). IEEE.
Rahimi, S., Dayal, U., & Castellanos, M. (2019). NoSQL data stores: Current trends and future directions. Journal of King Saud University-Computer and Information Sciences.
Gupta, P., & Khosla, A. (2020). A survey on NoSQL databases and its technologies. In 2020 4th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1230-1235). IEEE.
Smith, M., Jacobs, A., Raison, M., & Hand, S. (2018). The case for NoSQL over RDBMS for financial data. In Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (p. 15). ACM.
Wang, W., & Lo, D. (2017). An empirical study on adoption of NoSQL databases. Empirical Software Engineering, 22(6), 3125-3162.
Rahmat, R. F., Arshad, N. H., & Yahaya, J. (2018). A systematic literature review on NoSQL databases: New era of databases for big data. Journal of King Saud University-Computer and Information Sciences.
Mohanty, S., Jagadev, A. K., & Jena, D. (2019). A comparative study on NoSQL databases. In 2019 IEEE Calcutta Conference (CALCON) (pp. 164-168). IEEE.
Patil, S. R., & Jadhav, D. S. (2018). Survey on NoSQL databases: New era for storage systems. International Journal of Computer Applications, 183(18), 25-29.
Wiederhold, G. (2013). NoSQL evaluation. In 2013 46th Hawaii International Conference on System Sciences (pp. 3686-3695). IEEE.
Grolinger, K., Higashino, W. A., Capretz, M. A., & Kessel, M. (2013). Performance analysis of NoSQL databases. IEEE Transactions on Cloud Computing, 1(2), 149-161.
Mohanty, S., Jagadev, A. K., & Jena, D. (2019). A comprehensive survey on NoSQL databases. In Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies (pp. 33-43). Springer.
Kasula, B. Y. (2017). Machine Learning Unleashed: Innovations, Applications, and Impact Across Industries. International Transactions in Artificial Intelligence, 1(1), 1–7. Retrieved from https://isjr.co.in/index.php/ITAI/article/view/169
Kasula, B. Y. (2017). Transformative Applications of Artificial Intelligence in Healthcare: A Comprehensive Review. International Journal of Statistical Computation and Simulation, 9(1). Retrieved from https://journals.threws.com/index.php/IJSCS/article/view/215
Kasula, B. Y. (2018). Exploring the Efficacy of Neural Networks in Pattern Recognition: A Comprehensive Review. International Transactions in Artificial Intelligence, 2(2), 1–7. Retrieved from https://isjr.co.in/index.php/ITAI/article/view/170
Kasula, B. Y. (2019). Exploring the Foundations and Practical Applications of Statistical Learning. International Transactions in Machine Learning, 1(1), 1–8. Retrieved from https://isjr.co.in/index.php/ITML/article/view/176
Kasula, B. Y. (2019). Enhancing Classification Precision: Exploring the Power of Support-Vector Networks in Machine Learning. International Scientific Journal for Research, 1(1). Retrieved from https://isjr.co.in/index.php/ISJR/article/view/171
Nayak, M. S., & Sahoo, A. (2019). NoSQL databases: A comprehensive review. Journal of King Saud University-Computer and Information Sciences.
Fowler, M. (2013). NoSQL. In Patterns of Enterprise Application Architecture (pp. 321-344). Addison-Wesley.
Sadalage, P. J., Fowler, M., & Highsmith, J. (2012). NoSQL databases. IEEE Software, 29(6), 30-35.
Leavitt, N. (2010). Will NoSQL databases live up to their promise? Computer, 43(2), 12-14.
Patel, N., & Patel, N. (2015). An analytical study on NoSQL databases. International Journal of Advanced Research in Computer and Communication Engineering, 4(3), 224-230.
Kasula, B. Y. (2016). Advancements and Applications of Artificial Intelligence: A Comprehensive Review. International Journal of Statistical Computation and Simulation, 8(1), 1–7. Retrieved from https://journals.threws.com/index.php/IJSCS/article/view/214
Kasula, B. Y. (2020). Fraud Detection and Prevention in Blockchain Systems Using Machine Learning. (2020). International Meridian Journal, 2(2), 1-8. https://meridianjournal.in/index.php/IMJ/article/view/22