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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.01631 |
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| _version_ | 1866909447591297024 |
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| author | Liang, Chen Yang, Donghua Liang, Zheng Liang, Zhiyu Zhang, Tianle Xiao, Boyu Yang, Yuqing Wang, Wenqi Wang, Hongzhi |
| author_facet | Liang, Chen Yang, Donghua Liang, Zheng Liang, Zhiyu Zhang, Tianle Xiao, Boyu Yang, Yuqing Wang, Wenqi Wang, Hongzhi |
| contents | Data analysis focuses on harnessing advanced statistics, programming, and machine learning techniques to extract valuable insights from vast datasets. An increasing volume and variety of research emerged, addressing datasets of diverse modalities, formats, scales, and resolutions across various industries. However, experienced data analysts often find themselves overwhelmed by intricate details in ad-hoc solutions or attempts to extract the semantics of grounded data properly. This makes it difficult to maintain and scale to more complex systems. Pre-trained foundation models (PFMs), grounded with a large amount of grounded data that previous data analysis methods can not fully understand, leverage complete statistics that combine reasoning of an admissible subset of results and statistical approximations by surprising engineering effects, to automate and enhance the analysis process. It pushes us to revisit data analysis to make better sense of data with PFMs. This paper provides a comprehensive review of systematic approaches to optimizing data analysis through the power of PFMs, while critically identifying the limitations of PFMs, to establish a roadmap for their future application in data analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01631 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Revisiting Data Analysis with Pre-trained Foundation Models Liang, Chen Yang, Donghua Liang, Zheng Liang, Zhiyu Zhang, Tianle Xiao, Boyu Yang, Yuqing Wang, Wenqi Wang, Hongzhi Databases Data analysis focuses on harnessing advanced statistics, programming, and machine learning techniques to extract valuable insights from vast datasets. An increasing volume and variety of research emerged, addressing datasets of diverse modalities, formats, scales, and resolutions across various industries. However, experienced data analysts often find themselves overwhelmed by intricate details in ad-hoc solutions or attempts to extract the semantics of grounded data properly. This makes it difficult to maintain and scale to more complex systems. Pre-trained foundation models (PFMs), grounded with a large amount of grounded data that previous data analysis methods can not fully understand, leverage complete statistics that combine reasoning of an admissible subset of results and statistical approximations by surprising engineering effects, to automate and enhance the analysis process. It pushes us to revisit data analysis to make better sense of data with PFMs. This paper provides a comprehensive review of systematic approaches to optimizing data analysis through the power of PFMs, while critically identifying the limitations of PFMs, to establish a roadmap for their future application in data analysis. |
| title | Revisiting Data Analysis with Pre-trained Foundation Models |
| topic | Databases |
| url | https://arxiv.org/abs/2501.01631 |