Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
Effective data integration is crucial for organizations to manage and utilize vast, complex datasets. This paper presents a comprehensive framework for assessing Artificial Intelligence (AI)-based data integration tools, addressing the increasing demand for innovative solutions in this domain. Derived from a literature review, the framework encompasses 12 key dimensions including automation, data handling, support, and operational factors. Validated by industry practitioners, the framework demonstrates practical relevance and applicability. We assessed the derived key factors regarding their impact on the steps along the data integration process and applied the framework to evaluate 15 mature AI-based data integration tools. Our findings reveal that these tools reach high performances across the data integration process, however mostly focusing on single steps. Thereby, this study contributes to both theoretical understanding and practical tool selection, providing a robust foundation for future research and development in AI-driven data integration.
Recommended Citation
Schulz, Thimo; Weinreuter, Maria Madeleine; and Augenstein, Dominik, "Artificial Intelligence in Data Integration: A Comprehensive Framework and Tool Evaluation" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 3.
https://aisel.aisnet.org/hicss-58/da/big_data_and_analytics/3
Artificial Intelligence in Data Integration: A Comprehensive Framework and Tool Evaluation
Hilton Waikoloa Village, Hawaii
Effective data integration is crucial for organizations to manage and utilize vast, complex datasets. This paper presents a comprehensive framework for assessing Artificial Intelligence (AI)-based data integration tools, addressing the increasing demand for innovative solutions in this domain. Derived from a literature review, the framework encompasses 12 key dimensions including automation, data handling, support, and operational factors. Validated by industry practitioners, the framework demonstrates practical relevance and applicability. We assessed the derived key factors regarding their impact on the steps along the data integration process and applied the framework to evaluate 15 mature AI-based data integration tools. Our findings reveal that these tools reach high performances across the data integration process, however mostly focusing on single steps. Thereby, this study contributes to both theoretical understanding and practical tool selection, providing a robust foundation for future research and development in AI-driven data integration.
https://aisel.aisnet.org/hicss-58/da/big_data_and_analytics/3