Problem
Research questions and friction points this paper is trying to address.
Exploratory Unstructured Data Analysis
Human-AI Collaboration
Faceted Classifications
Semantic Categorization
Exploratory Unstructured Data Analysis: A Formative Study and Implications for Human-AI Collaboration
🤖 AI Summary
研究提出了一种探索性非结构化数据分析框架，通过用户研究和CLIP评估，识别了四个提高人机协作的机会。
📝 Abstract
We propose a conceptual framework for exploratory data analysis of (large) unstructured data (EluDA), combining classical elements (querying, visualization) with active knowledge construction in the "search for structure". In a formative study, users conceptualized a structure for an image dataset during exploration. We found that users conceptualize by building faceted classifications bottom-up and rarely create meaningful spatial categorization during this process. We also evaluated CLIP for zero-shot assignment and semantic categorization, finding that it remains unreliable for assigning user-defined concepts to images but does support semantic grouping. Based on these findings, we identify and discuss four key opportunities for human-AI collaboration in EluDA: intelligent sampling and visualization to maximize data visibility; incremental and few-shot learning to minimize effort for reliable assignment; automatic category, concept, and facet suggestions to reduce effort during the search for structure; and the necessity for effective trust calibration methods.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Exploratory Unstructured Data Analysis
Active Knowledge Construction
Human-AI Collaboration
Intelligent Sampling and Visualization
Automatic Suggestions for Categories
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