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| Hauptverfasser: | , , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2507.12677 |
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| _version_ | 1866911061184086016 |
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| author | Yoo, Sangbong Lee, Jaeyoung Yoon, Chanyoung Son, Geonyeong Hong, Hyein Seo, Seongbum Yim, Soobin Jung, Chanyoung Park, Jungsoo Kim, Misuk Jang, Yun |
| author_facet | Yoo, Sangbong Lee, Jaeyoung Yoon, Chanyoung Son, Geonyeong Hong, Hyein Seo, Seongbum Yim, Soobin Jung, Chanyoung Park, Jungsoo Kim, Misuk Jang, Yun |
| contents | Data heterogeneity is a prevalent issue, stemming from various conflicting factors, making its utilization complex. This uncertainty, particularly resulting from disparities in data formats, frequently necessitates the involvement of experts to find resolutions. Current methodologies primarily address conflicts related to data structures and schemas, often overlooking the pivotal role played by data transformation. As the utilization of artificial intelligence (AI) continues to expand, there is a growing demand for a more streamlined data preparation process, and data transformation becomes paramount. It customizes training data to enhance AI learning efficiency and adapts input formats to suit diverse AI models. Selecting an appropriate transformation technique is paramount in preserving crucial data details. Despite the widespread integration of AI across various industries, comprehensive reviews concerning contemporary data transformation approaches are scarce. This survey explores the intricacies of data heterogeneity and its underlying sources. It systematically categorizes and presents strategies to address heterogeneity stemming from differences in data formats, shedding light on the inherent challenges associated with each strategy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12677 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data Transformation Strategies to Remove Heterogeneity Yoo, Sangbong Lee, Jaeyoung Yoon, Chanyoung Son, Geonyeong Hong, Hyein Seo, Seongbum Yim, Soobin Jung, Chanyoung Park, Jungsoo Kim, Misuk Jang, Yun Machine Learning Artificial Intelligence Data heterogeneity is a prevalent issue, stemming from various conflicting factors, making its utilization complex. This uncertainty, particularly resulting from disparities in data formats, frequently necessitates the involvement of experts to find resolutions. Current methodologies primarily address conflicts related to data structures and schemas, often overlooking the pivotal role played by data transformation. As the utilization of artificial intelligence (AI) continues to expand, there is a growing demand for a more streamlined data preparation process, and data transformation becomes paramount. It customizes training data to enhance AI learning efficiency and adapts input formats to suit diverse AI models. Selecting an appropriate transformation technique is paramount in preserving crucial data details. Despite the widespread integration of AI across various industries, comprehensive reviews concerning contemporary data transformation approaches are scarce. This survey explores the intricacies of data heterogeneity and its underlying sources. It systematically categorizes and presents strategies to address heterogeneity stemming from differences in data formats, shedding light on the inherent challenges associated with each strategy. |
| title | Data Transformation Strategies to Remove Heterogeneity |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.12677 |