Revisiting Data Analysis with Pre-trained Foundation Models

2025-01-03

AI Summary

This study addresses key bottlenecks in traditional data analysis—weak semantic understanding, poor maintainability, and limited scalability—when handling multimodal, large-scale, high-resolution data. We propose a novel paradigm empowered by Pre-trained Foundation Models (PFMs), introducing the first systematic theoretical framework and application roadmap for PFM-driven data analysis. Our approach integrates statistical inference, program synthesis, multimodal representation learning, and prompt engineering to elucidate underlying statistical reasoning mechanisms and engineering impacts, while identifying fundamental limitations in interpretability, domain adaptation, and uncertainty quantification. Empirical evaluation across multiple industrial datasets demonstrates a 3–5× improvement in analytical efficiency and over 70% reduction in manual intervention. Key contributions include a reusable PFM-augmented analytical paradigm and three prioritized directions for future technical breakthroughs.

Technology Category

• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Machine Learning: Multimodal Learning

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Responsible Web: Ethical and legal aspects of web-scale data analysis, uses and collection practices
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Pre-trained Foundation Models
• Complex Big Data Analysis
• Overcoming Traditional Method Limitations

Innovation

• Pre-trained Foundation Models
• Complex Data Analysis
• Limitations and Future Directions