  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.
