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Relational tables, where each row corresponds to an entity and each columncorresponds to an attribute, have been the standard for tables in relationaldatabases. However, such a standard cannot be taken for granted when dealingwith tables "in the wild". Our survey of real spreadsheet-tables and web-tablesshows that over 30% of such tables do not conform to the relational standard,for which complex table-restructuring transformations are needed before thesetables can be queried easily using SQL-based analytics tools. Unfortunately,the required transformations are non-trivial to program, which has become asubstantial pain point for technical and non-technical users alike, asevidenced by large numbers of forum questions in places like StackOverflow andExcel/Power-BI/Tableau forums.  We develop an Auto-Tables system that can automatically synthesize pipelineswith multi-step transformations (in Python or other languages), to transformnon-relational tables into standard relational forms for downstream analytics,obviating the need for users to manually program transformations. We compile anextensive benchmark for this new task, by collecting 244 real test cases fromuser spreadsheets and online forums. Our evaluation suggests that Auto-Tablescan successfully synthesize transformations for over 70% of test cases atinteractive speeds, without requiring any input from users, making this aneffective tool for both technical and non-technical users to prepare data foranalytics.

Auto-Tables: Synthesizing Multi-Step Transformations to Relationalize Tables without Using Examples