Efficient Uncertainty Tracking for Complex Queries with Attribute-level Bounds (extended version)

TL;DR

This paper introduces attribute-annotated uncertain databases (AU-DBs) that enhance prior models by supporting attribute-level bounds and non-monotone queries, enabling more precise and scalable uncertain data management.

Contribution

The paper extends UA-DBs with attribute-level annotations and compact over-approximations, supporting complex queries and improving accuracy and efficiency in uncertain data processing.

Findings

Supports attribute-level bounds for more precise approximations

Enables handling of non-monotone queries like aggregation and set difference

Scales efficiently to large datasets with complex queries

Abstract

Certain answers are a principled method for coping with the uncertainty that arises in many practical data management tasks. Unfortunately, this method is expensive and may exclude useful (if uncertain) answers. Prior work introduced Uncertainty Annotated Databases (UA-DBs), which combine an under- and over-approximation of certain answers. UA-DBs combine the reliability of certain answers based on incomplete K-relations with the performance of classical deterministic database systems. However, UA-DBs only support a limited class of queries and do not support attribute-level uncertainty which can lead to inaccurate under-approximations of certain answers. In this paper, we introduce attribute-annotated uncertain databases (AU-DBs) which extend the UA-DB model with attribute-level annotations that record bounds on the values of an attribute across all possible worlds. This enables more precise approximations of incomplete databases. Furthermore, we extend UA-DBs to encode an compact over-approximation of possible answers which is necessary to support non-monotone queries including aggregation and set difference. We prove that query processing over AU-DBs preserves the bounds of certain and possible answers and investigate algorithms for compacting intermediate results to retain efficiency. Through an compact encoding of possible answers, our approach also provides a solid foundation for handling missing data. Using optimizations that trade accuracy for performance, our approach scales to complex queries and large datasets, and produces accurate results. Furthermore, it significantly outperforms alternative methods for uncertain data management.