Artificial Intelligence in Data Integration: A Comprehensive Framework and Tool Evaluation

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dc.contributor.authorSchulz, Thimo
dc.contributor.authorWeinreuter, Maria Madeleine
dc.contributor.authorAugenstein, Dominik
dc.date.accessioned2024-12-26T21:05:14Z
dc.date.available2024-12-26T21:05:14Z
dc.date.issued2025-01-07
dc.description.abstractEffective 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.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2025.122
dc.identifier.isbn978-0-9981331-8-8
dc.identifier.other842dffb4-1441-4b27-ad19-19c368014b3b
dc.identifier.urihttps://hdl.handle.net/10125/108960
dc.relation.ispartofProceedings of the 58th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBig Data and Analytics: The Path to Maturity
dc.subjectai-based data integration, automation, data integration tools, etl, tool search
dc.titleArtificial Intelligence in Data Integration: A Comprehensive Framework and Tool Evaluation
dc.typeConference Paper
dc.type.dcmiText
prism.startingpage1019

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