AUTOMATED DATA MAPPING FOR ENTERPRISE DATA INTEGRATION: LEVERAGING MACHINE LEARNING FOR EFFICIENT DATA TRANSFORMATION
DOI:
https://doi.org/10.34218/IJCET_16_01_268Keywords:
Automated Data Mapping, Data Integration, Enterprise Systems, Machine Learning, Schema MatchingAbstract
This comprehensive article explores the evolution and implementation of automated data mapping solutions in enterprise data integration. The article examines how organizations face challenges in integrating data across diverse sources and systems, highlighting the limitations of traditional manual mapping approaches. The article investigates how machine learning algorithms and advanced automation techniques are transforming data integration processes, offering improved accuracy, efficiency, and cost-effectiveness. Through analysis of various sectors including healthcare, manufacturing, and construction, the article demonstrates the significant benefits of automated mapping solutions in handling complex data relationships, reducing errors, and accelerating integration timelines. The article also addresses critical implementation considerations, including data quality assessment, training requirements, and system integration challenges, while exploring future directions in automated mapping technologies through deep learning and intelligent automation applications.
References
John Krogstie, "Capturing Enterprise Data Integration Challenges Using a Semiotic Data Quality Framework," Business & Information Systems Engineering 57(1), 2015. Available: https://www.researchgate.net/publication/273160558_Capturing_Enterprise_Data_Integration_Challenges_Using_a_Semiotic_Data_Quality_Framework
Abhishek Vajpayee, "The Role of Machine Learning in Automated Data Pipelines and Warehousing: Enhancing Data Integration, Transformation, and Analytics," ESP Journal of Engineering & Technology Advancements, 2023. Available: https://espjeta.org/Volume3-Issue3/JETA-V3I7P111.pdf
Ariyan Fazlollahi, et al., "Measuring the impact of enterprise integration on firm performance using data envelopment analysis," International Journal of Production Economics, Volume 200, June 2018, Pages 119-129. Available: https://www.sciencedirect.com/science/article/abs/pii/S0925527318301002
Ravi Teja Gurram, "Enterprise AI and Automation Integration: A Technical Framework for Modern Business Intelligence Systems,"International Journal for Multidisciplinary Research (IJFMR), Volume 6, Issue 6, November-December 2024. Available: https://www.ijfmr.com/papers/2024/6/30952.pdf
Do Hong Hai, "Schema matching and mapping-based data integration," ACM Computing Surveys, vol. 43, no. 2, pp. 123-157, Jan. 2014. Available: https://www.researchgate.net/publication/266980963_Schema_matching_and_mapping-based_data_integration
Umair Shafique, et al., "A Comprehensive Study on Natural Language Processing and Natural Language Interface to Databases," International Journal of Computer Applications, vol. 56, no. 3, pp. 1-8, Oct. 2014. Available: https://www.researchgate.net/publication/266310694_A_Comprehensive_Study_on_Natural_Language_Processing_and_Natural_Language_Interface_to_Databases
Alhassan Mumuni, et al., "Automated data processing and feature engineering for deep learning and big data applications: A survey," Journal of Information and Intelligence, Volume 3, Issue 2, March 2025, Pages 113-153. Available: https://www.sciencedirect.com/science/article/pii/S2949715924000027
Solomon Lartey, "A Comparative Analysis of Automatic and Manual Systems in Modern Technology," International Journal of Industrial Automation, vol. 12, no. 4, pp. 234-251, 2024. Available: https://www.researchgate.net/publication/383945474_A_Comparative_Analysis_of_Automatic_and_Manual_Systems_in_Modern_Technology
Andreas Hilbert, "Critical Success Factors for Data Mining Projects," Data Analysis and Decision Support (pp.231-240), 2005. Available: https://www.researchgate.net/publication/226123485_Critical_Success_Factors_for_Data_Mining_Projects
Weimin Wang, et al., "Automated point mapping for building control systems: Recent advances and future research needs," Automation in Construction, Volume 85, January 2018, Pages 107-123. Available: https://www.sciencedirect.com/science/article/abs/pii/S0926580517300018
Heena Satam, et al., "Next-Generation Sequencing Technology: Current Trends and Advancements," Biology (Basel). 2023 . Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC10376292/
Dena Shamsollahi, et al., "Data integration using deep learning and real-time locating system (RTLS) for automated construction progress monitoring and reporting," Automation in Construction, Volume 168, Part A, 1 December 2024, 105778. Available: https://www.sciencedirect.com/science/article/pii/S0926580524005144
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Vijaya Bhaskara reddy Soperla (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
