We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| lipengcs/auto-tables-benchmark | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| compute_hit_at_k | Ran | lipengcs/auto-tables-benchmark/evaluate.py pointer only (licence: NONE) · get_code("5f6e91456346ad80") |
| is_hit | Ran | lipengcs/auto-tables-benchmark/evaluate.py pointer only (licence: NONE) · get_code("efc21a25127a1988") |
| listdir | Ran | lipengcs/auto-tables-benchmark/evaluate.py pointer only (licence: NONE) · get_code("cca0d3226c262115") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Relational tables, where each row corresponds to an entity and each column corresponds to an attribute, have been the standard for tables in relational databases. However, such a standard cannot be taken for granted when dealing with tables "in the wild". Our survey of real spreadsheet-tables and web-tables shows that over 30% of such tables do not conform to the relational standard, for which complex table-restructuring transformations are needed before these tables can be queried easily using SQL-based analytics tools. Unfortunately, the required transformations are non-trivial to program, which has become a substantial pain point for technical and non-technical users alike, as evidenced by large numbers of forum questions in places like StackOverflow and Excel/Power-BI/Tableau forums. We develop an Auto-Tables system that can automatically synthesize pipelines with multi-step transformations (in Python or other languages), to transform non-relational tables into standard relational forms for downstream analytics, obviating the need for users to manually program transformations. We compile an extensive benchmark for this new task, by collecting 244 real test cases from user spreadsheets and online forums. Our evaluation suggests that Auto-Tables can successfully synthesize transformations for over 70% of test cases at interactive speeds, without requiring any input from users, making this an effective tool for both technical and non-technical users to prepare data for analytics.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2307.14565")
get_code_for_paper("2307.14565")
have("2307.14565")
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