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.2020.1
URN: urn:nbn:de:0030-drops-119258
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2020/11925/
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Kimelfeld, Benny

Facets of Probabilistic Databases (Invited Talk)

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LIPIcs-ICDT-2020-1.pdf (0.2 MB)

Abstract

Probabilistic databases are commonly known in the form of the tuple-independent model, where the validity of every tuple is an independent random event. Conceptually, the notion is more general, as a probabilistic database refers to any probability distribution over ordinary databases. A central computational problem is that of marginal inference for database queries: what is the probability that a given tuple is a query answer? In this talk, I will discuss recent developments in several research directions that, collectively, position probabilistic databases as the common and natural foundation of various challenges at the core of data analytics. Examples include reasoning about uncertain preferences from conventional distributions such as the Mallows model, data cleaning and repairing in probabilistic paradigms such as the HoloClean system, and the explanation of query answers through concepts from cooperative game theory such as the Shapley value and the Banzhaf Power Index. While these challenges manifest different facets of probabilistic databases, I will show how they interrelate and, moreover, how they relate to the basic theory of inference over tuple-independent databases.

BibTeX - Entry

@InProceedings{kimelfeld:LIPIcs:2020:11925,
  author =	{Benny Kimelfeld},
  title =	{{Facets of Probabilistic Databases (Invited Talk)}},
  booktitle =	{23rd International Conference on Database Theory (ICDT 2020)},
  pages =	{1:1--1:1},
  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/2020/11925},
  URN =		{urn:nbn:de:0030-drops-119258},
  doi =		{10.4230/LIPIcs.ICDT.2020.1},
  annote =	{Keywords: Probabilistic databases, data cleaning, preference models, Shapley value}
}

Keywords: Probabilistic databases, data cleaning, preference models, Shapley value
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/10.5446/46787

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