文章基本信息

标题：INFINITE PROBABILISTIC DATABASES
本地全文：下载
作者：Martin Grohe ; Peter Lindner
期刊名称：Logical Methods in Computer Science
印刷版ISSN：1860-5974
电子版ISSN：1860-5974
出版年度：2022
卷号：18
期号：1
页码：1-43
DOI：10.46298/lmcs-18(1:34)2022
语种：English
出版社：Technical University of Braunschweig
摘要：Probabilistic databases (PDBs) model uncertainty in data in a quantitative way. In the established formal framework, probabilistic (relational) databases are finite probability spaces over relational database instances. This finiteness can clash with intuitive query behavior (Ceylan et al., KR 2016), and with application scenarios that are better modeled by continuous probability distributions (Dalvi et al., CACM 2009). We formally introduced infinite PDBs in (Grohe and Lindner, PODS 2019) with a primary focus on countably infinite spaces. 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. We argue that finite point processes are an appropriate model from probability theory for dealing with general probabilistic databases. This 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.
关键词：Probabilistic Databases;Possible Worlds Semantics;Query Measurability;Relational Algebra;Aggregation;Infinite Probabilistic Databases