2016 IEEE 32nd International Conference on Data Engineering (ICDE) (2016)
May 16, 2016 to May 20, 2016
Daniel Deutch , Computer Science Department, Tel Aviv University, Israel
Amir Gilad , Computer Science Department, Tel Aviv University, Israel
To assist non-specialists in formulating database queries, multiple frameworks that automatically infer queries from a set of input and output examples have been proposed. While highly useful, a shortcoming of the approach is that if users can only provide a small set of examples, many inherently different queries may qualify. We observe that additional information about the examples, in the form of their explanations, is useful in significantly focusing the set of qualifying queries. We propose to demonstrate QPlain, a system that learns conjunctive queries from examples and their explanations. We capture explanations of different levels of granularity and detail, by leveraging recently developed models for data provenance. Explanations are fed through an intuitive interface, are compiled to the appropriate provenance model, and are then used to derive proposed queries. We will demonstrate that it is feasible for non-specialists to provide examples with meaningful explanations, and that the presence of such explanations result in a much more focused set of queries which better match user intentions.
Databases, Planning, Standards, Context, Algorithm design and analysis, Graphical user interfaces, Computer science
D. Deutch and A. Gilad, "QPlain: Query by explanation," 2016 IEEE 32nd International Conference on Data Engineering (ICDE), Helsinki, Finland, 2016, pp. 1358-1361.