QPlain: Query by explanation

Daniel Deutch, Amir Gilad

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1358-1361
Number of pages4
ISBN (Electronic)9781509020195
DOIs
StatePublished - 22 Jun 2016
Externally publishedYes
Event32nd IEEE International Conference on Data Engineering, ICDE 2016 - Helsinki, Finland
Duration: 16 May 201620 May 2016

Publication series

Name2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016

Conference

Conference32nd IEEE International Conference on Data Engineering, ICDE 2016
Country/TerritoryFinland
CityHelsinki
Period16/05/1620/05/16

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