Sort it Like You Mean It: Discovering Semantically Interesting Attribute Augmentations to Sort Tables

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Abstract

Sorting is a fundamental operation in table analysis. Data scientists frequently sort tables to uncover key insights—for example, identifying the top 10 products by sales. However, this process is largely manual. Data scientists must (1) understand the semantics behind the sorting they wish to apply, and (2) ensure the necessary attributes are present—often requiring manual augmentation of the table. But what if data scientists could receive suggestions for semantically meaningful ways to sort a table, powered by automatic augmentations from a data lake? In this demo, we present InsightSort, an end-to-end system that recommends attribute augmentations to enable richer, more insightful sorting for table exploration. InsightSort works by: (1) discovering potential augmentations by linking the input table with relevant data lake tables, and (2) leveraging a Large Language Model (LLM) to synthesize the top-k diverse sorting attributes based on their semantics. A companion video is available at [1].