TY - GEN
T1 - QPlain
T2 - 32nd IEEE International Conference on Data Engineering, ICDE 2016
AU - Deutch, Daniel
AU - Gilad, Amir
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/22
Y1 - 2016/6/22
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84980325821&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2016.7498344
DO - 10.1109/ICDE.2016.7498344
M3 - Conference contribution
AN - SCOPUS:84980325821
T3 - 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
SP - 1358
EP - 1361
BT - 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
PB - Institute of Electrical and Electronics Engineers
Y2 - 16 May 2016 through 20 May 2016
ER -