# Infinite Probabilistic Databases

## TL;DR

This paper extends probabilistic databases to uncountable spaces using finite point processes, addressing foundational issues and ensuring well-defined semantics for queries involving continuous distributions.

## Contribution

It introduces a framework for uncountable probabilistic databases using finite point processes, ensuring measurability and well-defined query semantics.

## Key findings

- Measurability of relational algebra queries established
- Framework supports continuous probability distributions in PDBs
- Ensures well-defined semantics for aggregate and Datalog queries

## Abstract

Probabilistic databases (PDBs) are used to model uncertainty in data in a quantitative way. In the standard formal framework, PDBs are finite probability spaces over relational database instances. It has been argued convincingly that this is not compatible with an open world semantics (Ceylan et al., KR 2016) and with application scenarios that are modeled by continuous probability distributions (Dalvi et al., CACM 2009). We recently introduced a model of PDBs as infinite probability spaces that addresses these issues (Grohe and Lindner, PODS 2019). While that work was mainly concerned with countably infinite probability spaces, our focus here is on uncountable spaces. Such an extension is necessary to model typical continuous probability distributions that appear in many applications. However, an extension beyond countable probability spaces raises nontrivial foundational issues concerned with the measurability of events and queries and ultimately with the question whether queries have a well-defined semantics. It turns out that so-called finite point processes are the appropriate model from probability theory for dealing with probabilistic databases. This model allows us to construct suitable (uncountable) probability spaces of database instances in a systematic way. Our main technical results are measurability statements for relational algebra queries as well as aggregate queries and datalog queries.