Database Aggregation

TL;DR

This paper explores methods for aggregating multiple databases modeled as first-order structures, ensuring integrity constraints are preserved and query answers are consistent post-aggregation, bridging rational choice theory and database knowledge representation.

Contribution

It characterizes classes of aggregators that preserve integrity constraints and identifies query languages with consistent answers after aggregation.

Findings

Identified aggregators that respect integrity constraints.

Characterized query languages with consistent answers post-aggregation.

Bridged rational choice theory with database aggregation techniques.

Abstract

Knowledge can be represented compactly in a multitude ways, from a set of propositional formulas, to a Kripke model, to a database. In this paper we study the aggregation of information coming from multiple sources, each source submitting a database modelled as a first-order relational structure. In the presence of an integrity constraint, we identify classes of aggregators that respect it in the aggregated database, provided all individual databases satisfy it. We also characterise languages for first-order queries on which the answer to queries on the aggregated database coincides with the aggregation of the answers to the query obtained on each individual database. This contribution is meant to be a first step on the application of techniques from rational choice theory to knowledge representation in databases.