ARTIFICIAL INTELLIGENCE IN DATA INTEGRATION: ADDRESSING SCALABILITY, SECURITY, AND REAL-TIME PROCESSING CHALLENGES
Keywords:
Artificial Intelligence (AI), Machine Learning (ML), Data Integration, Data Consolidation, Data VirtualizationAbstract
This article examines the pivotal role of Artificial Intelligence (AI) in addressing data integration challenges faced by large enterprises. As organizations grapple with an ever-increasing volume and diversity of data sources, AI and Machine Learning (ML) technologies are emerging as critical solutions for efficient data management. The article explores three primary forms of data integration—consolidation, virtualization, and propagation—and their significance in contemporary data environments. It analyzes how AI techniques are revolutionizing data mapping, quality enhancement, and real-time processing, thereby mitigating key issues such as data silos, incompatibility, and scalability concerns. Through a series of case studies, the article demonstrates AI's transformative impact on data integration practices across various industries. The article further investigates how AI-driven data integration is reshaping strategic decision-making processes and operational efficiencies in enterprises. By synthesizing current developments and future trends, this article provides valuable insights for data professionals and business leaders seeking to leverage AI for improved data integration and management strategies.
References
F. Naumann, "Data Profiling Revisited," ACM SIGMOD Record, vol. 42, no. 4, pp. 40-49, Dec. 2013. [Online]. Available: https://doi.org/10.1145/2590989.2590995
Q. Liu, P. Li, W. Zhao, W. Cai, S. Yu, and V. C. M. Leung, "A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View," IEEE Access, vol. 6, pp. 12103-12117, 2018. [Online]. Available: https://ieeexplore.ieee.org/document/8290925
T. Dasu and T. Johnson, "Exploratory Data Mining and Data Cleaning," John Wiley & Sons, 2003. [Online]. Available: https://doi.org/10.1002/0471448354
S. Chakraborty, S. Tomsett, R. Raghavendra, D. Harborne, M. Alzantot, F. Cerutti, M. Srivastava, A. Preece, S. Julier, R. M. Rao, T. D. Kelley, D. Braines, M. Sensoy, C. J. Willis, and P. Gurram, "Interpretability of deep learning models: A survey of results," in 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation, 2017, pp. 1-6. [Online]. Available: https://ieeexplore.ieee.org/document/8397411
S. Makridakis, "The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms," Futures, vol. 90, pp. 46-60, 2017. [Online]. Available: https://doi.org/10.1016/j.futures.2017.03.006
A. Doan, A. Halevy, and Z. Ives, "Principles of Data Integration," Morgan Kaufmann, 2012. [Online]. Available: https://doi.org/10.1016/C2011-0-06130-6
X. L. Dong and T. Rekatsinas, "Data Integration and Machine Learning: A Natural Synergy," in Proceedings of the 2018 International Conference on Management of Data (SIGMOD '18), 2018, pp. 1645-1650. [Online]. Available: https://doi.org/10.1145/3183713.3197387
F. Naumann and M. Herschel, "An Introduction to Duplicate Detection," Synthesis Lectures on Data Management, vol. 2, no. 1, pp. 1-87, 2010. [Online]. Available: https://doi.org/10.2200/S00262ED1V01Y201003DTM003
M. Stonebraker, I. F. Ilyas, "Data Integration: The Current Status and the Way Forward," IEEE Data Eng. Bull., vol. 41, no. 2, pp. 3-9, 2018. [Online]. Available: http://sites.computer.org/debull/A18june/p3.pdf
A. Halevy, F. Korn, N. F. Noy, C. Olston, N. Polyzotis, S. Roy, and S. E. Whang, "Goods: Organizing Google's Datasets," in Proceedings of the 2016 International Conference on Management of Data, 2016, pp. 795-806. [Online]. Available: https://doi.org/10.1145/2882903.2903730
S. Ransbotham, D. Kiron, P. Gerbert, and M. Reeves, "Reshaping Business With Artificial Intelligence: Closing the Gap Between Ambition and Action," MIT Sloan Management Review, vol. 59, no. 1, pp. 1-17, 2017. [Online]. Available: https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/
M. M. Gobble, "Big Data: The Next Big Thing in Innovation," Research-Technology Management, vol. 56, no. 1, pp. 64-66, 2013. [Online]. Available: https://doi.org/10.5437/08956308X5601005
