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 language | English |
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Title of host publication | 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1358-1361 |
Number of pages | 4 |
ISBN (Electronic) | 9781509020195 |
DOIs | |
State | Published - 22 Jun 2016 |
Externally published | Yes |
Event | 32nd IEEE International Conference on Data Engineering, ICDE 2016 - Helsinki, Finland Duration: 16 May 2016 → 20 May 2016 |
Publication series
Name | 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016 |
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Conference
Conference | 32nd IEEE International Conference on Data Engineering, ICDE 2016 |
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Country/Territory | Finland |
City | Helsinki |
Period | 16/05/16 → 20/05/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.