SQuID is a query-by-example style system for semantic similarity-aware query intent discovery. The system takes a few example tuples as input and infers the user's intended query in SQL.

Video Demo


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SQuID SIGMOD Demo Poster

Project Summary

Recent expansion of database technology demands a convenient framework for non-expert users to explore datasets. Several approaches exist to assist these non-expert users where they can express their query intent by providing example tuples for their intended query output. However, these approaches treat the structural similarity among the example tuples as the only factor specifying query intent and ignore the richer context present in the data. SQuID is a system for Semantic similarity-aware Query Intent Discovery which takes a few example tuples from the user as input, through a simple interface, and consults the database to discover deeper associations among these examples. These data-driven associations reveal the semantic context of the provided examples, allowing SQuID to infer the user's intended query precisely and effectively. SQuID further explains its inference, by displaying the discovered semantic context to the user, who can then provide feedback and tune the result. SQuID can capture esoteric and complex semantic contexts and thus alleviates the need for constructing complex SQL queries, while not requiring the user to have any schema or query language knowledge.




Coming soon.


This work is supported by the National Science Foundation under grants IIS-1421322 and IIS-1453543.