Impact of Generative AI on Data Integration

  IJCTT-book-cover
 
         
 
© 2023 by IJCTT Journal
Volume-71 Issue-6
Year of Publication : 2023
Authors : Anshumali Ambasht
DOI :  10.14445/22312803/IJCTT-V71I6P109

How to Cite?

Anshumali Ambasht, "Impact of Generative AI on Data Integration," International Journal of Computer Trends and Technology, vol. 71, no. 6, pp. 55-56, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I6P109

Abstract
In recent years, generative artificial intelligence (AI) has emerged as a transformative technology with far-reaching implications for various fields. One area that has witnessed a significant impact is data integration, which involves combining and consolidating data from disparate sources. Generative AI, powered by deep learning models, has the ability to generate new and realistic data based on existing patterns and examples. This article explores the effects of generative AI on data integration, examining both the opportunities it presents and the challenges it poses.

Keywords
AI, Data integration, Data transformation, Data quality and Challenges.

Reference

[1] Erhard Rahm, and Hong Hai Do, “Data Cleaning: Problems and Current Approaches,” Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, vol. 23, no. 4, pp. 3-13, 2020.
[Google Scholar] [Publisher Link]
[2] Maurizio Lenzerini, “Data Integration: A Theoretical Perspective,” Proceedings of the Twenty-First ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 233-246, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Christine Parent, Stefano Spaccapietra, and Esteban Zimányi, Conceptual Modeling for Traditional and Spatio-Temporal Applications: The MADS Approach, Springer, pp. 106-121, 2006.
[Google Scholar] [Publisher Link]
[4] Alon Halevy, Anand Rajaraman, and Joann Ordille, “Data Integration: The Teenage Years,” 32nd International Conference on Very Large Databases, pp. 9-16, 2006.
[Google Scholar] [Publisher Link]
[5] Chris Giannella et al., “Mining Frequent Patterns in Data Streams at Multiple Time Granularities,” Next Generation Data Mining, vol. 212, pp. 191-212, 2003.
[Google Scholar] [Publisher Link]
[6] Missier, P., & Goble, C. (2008). Data integration: A theoretical perspective. In Handbook of Semantic Web Technologies (pp. 407- 434). Springer.
[7] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, “Deep Learning,” Nature, vol. 521, pp. 436-444, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Tero Karras et al., “Progressive Growing of GANs for Improved Quality, Stability, and Variation,” Neural and Evolutionary Computing, arXiv Preprint, 2018.
[CrossRef] [Google Scholar] [Publisher Link]