Towards Inferring Queries from Simple and Partial Provenance Examples

TL;DR

This paper introduces a two-step framework for inferring queries from a few output examples and explanations, using provenance conversion and a graph-based algorithm, making it accessible for non-experts.

Contribution

It presents a novel approach combining provenance conversion and graph algorithms to improve query inference from limited examples and explanations.

Findings

Promising initial experimental results

Effective inference with minimal examples and explanations

Framework suitable for non-expert users

Abstract

The field of query-by-example aims at inferring queries from output examples given by non-expert users, by finding the underlying logic that binds the examples. However, for a very small set of examples, it is difficult to correctly infer such logic. To bridge this gap, previous work suggested attaching explanations to each output example, modeled as provenance, allowing users to explain the reason behind their choice of example. In this paper, we explore the problem of inferring queries from a few output examples and intuitive explanations. We propose a two step framework: (1) convert the explanations into (partial) provenance and (2) infer a query that generates the output examples using a novel algorithm that employs a graph based approach. This framework is suitable for non-experts as it does not require the specification of the provenance in its entirety or an understanding of its structure. We show promising initial experimental results of our approach.