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 g k-budgeted diverse pivot tables, overcoming the shortcomings of prior work for top-k recommendations that cause redundancy. SAGE ensures that each pivot table is insightful , interpretable , and adaptive to the user's actions and preferences, while also guaranteeing that the set of pivot tables are different from each other, offering a diverse recommendation. We make two key technical contributions: (1) a data-semantics-aware model to measure the utility of a single pivot table and the diversity of a set of pivot tables, and (2) a scalable greedy algorithm that can efficiently select a set of diverse pivot tables of high utility, by leveraging data semantics to significantly reduce the combinatorial search space. Our extensive experiments on four real-world datasets show that SAGE outperforms alternative approaches, and efficiently scales to accommodate high-dimensional datasets. Additionally, through multiple case studies, we demonstrate SAGE's qualitative superiority over existing tools, and through a user study, we validate its practical usefulness and alignment with user preferences.