Learning and Verifying Quantified Boolean Queries by Example

TL;DR

This paper develops efficient algorithms for learning and verifying a specific class of complex quantified Boolean database queries by asking a minimal number of questions, improving user query specification and verification.

Contribution

It introduces optimal polynomial-question and polynomial-time algorithms for learning and verifying qhorn queries, a class of Boolean quantified queries, under certain conditions.

Findings

Algorithms require a polynomial number of questions for learning and verification.

The algorithms operate in polynomial time.

Effective for subclasses with bounded causal density.

Abstract

To help a user specify and verify quantified queries --- a class of database queries known to be very challenging for all but the most expert users --- one can question the user on whether certain data objects are answers or non-answers to her intended query. In this paper, we analyze the number of questions needed to learn or verify qhorn queries, a special class of Boolean quantified queries whose underlying form is conjunctions of quantified Horn expressions. We provide optimal polynomial-question and polynomial-time learning and verification algorithms for two subclasses of the class qhorn with upper constant limits on a query's causal density.