Fast and Simple Relational Processing of Uncertain Data

TL;DR

This paper presents U-relations, a relational model for uncertain data that enables efficient query processing and scales well with large, uncertain datasets using standard database techniques.

Contribution

Introduction of U-relations, a succinct relational model for attribute-level uncertain data, enabling scalable and efficient query evaluation.

Findings

Query evaluation on U-relations scales to large datasets.

Standard relational database techniques effectively optimize U-relation queries.

U-relations preserve query size and complexity during translation.

Abstract

This paper introduces U-relations, a succinct and purely relational representation system for uncertain databases. U-relations support attribute-level uncertainty using vertical partitioning. If we consider positive relational algebra extended by an operation for computing possible answers, a query on the logical level can be translated into, and evaluated as, a single relational algebra query on the U-relation representation. The translation scheme essentially preserves the size of the query in terms of number of operations and, in particular, number of joins. Standard techniques employed in off-the-shelf relational database management systems are effective for optimizing and processing queries on U-relations. In our experiments we show that query evaluation on U-relations scales to large amounts of data with high degrees of uncertainty.