Uncertainty Annotated Databases - A Lightweight Approach for Approximating Certain Answers (extended version)

TL;DR

This paper introduces Uncertainty Annotated Databases (UA-DBs), a lightweight method that efficiently approximates certain answers by combining under- and over-approximations, balancing reliability and utility in uncertain data management.

Contribution

The paper presents UA-DBs, a novel approach that generalizes incomplete databases using incomplete K-relations to efficiently approximate certain answers with higher utility.

Findings

Efficiently produces tight approximations of certain answers.

Balances reliability with inclusion of useful uncertain answers.

Demonstrates high utility through experimental evaluation.

Abstract

Certain answers are a principled method for coping with uncertainty that arises in many practical data management tasks. Unfortunately, this method is expensive and may exclude useful (if uncertain) answers. Thus, users frequently resort to less principled approaches to resolve the uncertainty. In this paper, we propose Uncertainty Annotated Databases (UA-DBs), which combine an under- and over-approximation of certain answers to achieve the reliability of certain answers, with the performance of a classical database system. Furthermore, in contrast to prior work on certain answers, UA-DBs achieve a higher utility by including some (explicitly marked) answers that are not certain. UA-DBs are based on incomplete K-relations, which we introduce to generalize the classical set-based notions of incomplete databases and certain answers to a much larger class of data models. Using an implementation of our approach, we demonstrate experimentally that it efficiently produces tight approximations of certain answers that are of high utility.