License: Creative Commons Attribution 3.0 Unported license (CC-BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ICDT.2020.16
URN: urn:nbn:de:0030-drops-119400
URL: https://drops.dagstuhl.de/opus/volltexte/2020/11940/
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Grohe, Martin ; Lindner, Peter

Infinite Probabilistic Databases

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LIPIcs-ICDT-2020-16.pdf (0.6 MB)


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.

BibTeX - Entry

@InProceedings{grohe_et_al:LIPIcs:2020:11940,
  author =	{Martin Grohe and Peter Lindner},
  title =	{{Infinite Probabilistic Databases}},
  booktitle =	{23rd International Conference on Database Theory (ICDT 2020)},
  pages =	{16:1--16:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-139-9},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{155},
  editor =	{Carsten Lutz and Jean Christoph Jung},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2020/11940},
  URN =		{urn:nbn:de:0030-drops-119400},
  doi =		{10.4230/LIPIcs.ICDT.2020.16},
  annote =	{Keywords: Probabilistic Databases, Possible Worlds Semantics, Query Measurability, Relational Algebra, Aggregate Queries}
}

Keywords: Probabilistic Databases, Possible Worlds Semantics, Query Measurability, Relational Algebra, Aggregate Queries
Collection: 23rd International Conference on Database Theory (ICDT 2020)
Issue Date: 2020
Date of publication: 11.03.2020
Supplementary Material: Video of the Presentation: https://doi.org/10.5446/46840


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