Data-Semantics-Aware Recommendation of Diverse Pivot Tables

Data summarization is essential to discover insights from large datasets. In spreadsheets, pivot tables offer a convenient way to summarize tabular data by computing aggregates over some attributes, grouped by others. However, identifying attribute combinations that will result in useful pivot tables remains a challenge, especially for high-dimensional datasets. We formalize the problem of automatically recommending insightful and interpretable pivot tables, eliminating the tedious manual process. A crucial aspect of recommending a set of pivot tables is to diversify them. Traditional work inadequately address the table-diversification problem, which leads us to the problem of pivot table diversification . We present SAGE, a data- s emantics- a ware system for recommendin