  Traditional relational data interfaces require precise structured queries
over potentially complex schemas. These rigid data retrieval mechanisms pose
hurdles for non-expert users, who typically lack language expertise and are
unfamiliar with the details of the schema. Query by Example (QBE) methods offer
an alternative mechanism: users provide examples of their intended query output
and the QBE system needs to infer the intended query. However, these approaches
focus on the structural similarity of the examples and ignore the richer
context present in the data. As a result, they typically produce queries that
are too general, and fail to capture the user's intent effectively. In this
paper, we present SQuID, a system that performs semantic similarity-aware query
intent discovery. Our work makes the following contributions: (1) We design an
end-to-end system that automatically formulates select-project-join queries in
an open-world setting, with optional group-by aggregation and intersection
operators; a much larger class than prior QBE techniques. (2) We express the
problem of query intent discovery using a probabilistic abduction model, that
infers a query as the most likely explanation of the provided examples. (3) We
introduce the notion of an abduction-ready database, which precomputes semantic
properties and related statistics, allowing SQuID to achieve real-time
performance. (4) We present an extensive empirical evaluation on three
real-world datasets, including user-intent case studies, demonstrating that
SQuID is efficient and effective, and outperforms machine learning methods, as
well as the state-of-the-art in the related query reverse engineering problem.
