Quantifying Uncertainty in Aggregate Queries over Integrated Datasets

TL;DR

This paper introduces a polynomial-time method to quantify uncertainty in aggregate query results over integrated datasets, aiding users in understanding the impact of data matching choices.

Contribution

It provides a novel approach to compute maximal and minimal query outcome ranges in data integration, improving robustness of uncertainty estimates.

Findings

Uncertainty estimates are more robust with graph-matching based data integration.

The method computes query outcome ranges efficiently in constrained scenarios.

Evaluation on real and synthetic datasets demonstrates practical effectiveness.

Abstract

Data integration is a notoriously difficult and heuristic-driven process, especially when ground-truth data are not readily available. This paper presents a measure of uncertainty by providing maximal and minimal ranges of a query outcome in two-table, one-to-many data integration workflows. Users can use these query results to guide a search through different matching parameters, similarity metrics, and constraints. Even though there are exponentially many such matchings, we show that in appropriately constrained circumstances that this result range can be calculated in polynomial time with bipartite graph matching. We evaluate this on real-world datasets and synthetic datasets, and find that uncertainty estimates are more robust when a graph-matching based approach is used for data integration.