ASTA: Learning Analytical Semantics over Tables for Intelligent Data Analysis and Visualization
This work addresses the need for explainable and generalizable automated recommendations in data analysis tools, though it appears incremental by extending existing methods to include conditional formatting.
The paper tackles the problem of automating intelligent data analysis and visualization from tables by proposing the ASTA framework, which uses analytical semantics to recommend charts and conditional formatting, achieving a recall at top 1 of 62.86% on public chart corpora and 72.31% on a collected corpus, outperforming baselines by about 14%.
Intelligent analysis and visualization of tables use techniques to automatically recommend useful knowledge from data, thus freeing users from tedious multi-dimension data mining. While many studies have succeeded in automating recommendations through rules or machine learning, it is difficult to generalize expert knowledge and provide explainable recommendations. In this paper, we present the recommendation of conditional formatting for the first time, together with chart recommendation, to exemplify intelligent table analysis. We propose analytical semantics over tables to uncover common analysis pattern behind user-created analyses. Here, we design analytical semantics by separating data focus from user intent, which extract the user motivation from data and human perspective respectively. Furthermore, the ASTA framework is designed by us to apply analytical semantics to multiple automated recommendations. ASTA framework extracts data features by designing signatures based on expert knowledge, and enables data referencing at field- (chart) or cell-level (conditional formatting) with pre-trained models. Experiments show that our framework achieves recall at top 1 of 62.86% on public chart corpora, outperforming the best baseline about 14%, and achieves 72.31% on the collected corpus ConFormT, validating that ASTA framework is effective in providing accurate and explainable recommendations.