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The Consistency of Probabilistic Databases with Independent Cells

Authors Amir Gilad, Aviram Imber, Benny Kimelfeld

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Author Details

Amir Gilad
  • Duke University, Durham, NC, USA
Aviram Imber
  • Technion - Israel Institute of Technology, Haifa, Israel
Benny Kimelfeld
  • Technion - Israel Institute of Technology, Haifa, Israel

Funding

The work of Amir Gilad was supported by NSF awards IIS-1703431, IIS-1552538, and IIS-2008107. The work of Aviram Imber and Benny Kimelfeld was supported by the Israel Science Foundation (ISF), Grant 768/19, and the German Research Foundation (DFG) Project 412400621 (DIP program).

Cite AsGet BibTex

Amir Gilad, Aviram Imber, and Benny Kimelfeld. The Consistency of Probabilistic Databases with Independent Cells. In 26th International Conference on Database Theory (ICDT 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 255, pp. 22:1-22:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.ICDT.2023.22

Abstract

A probabilistic database with attribute-level uncertainty consists of relations where cells of some attributes may hold probability distributions rather than deterministic content. Such databases arise, implicitly or explicitly, in the context of noisy operations such as missing data imputation, where we automatically fill in missing values, column prediction, where we predict unknown attributes, and database cleaning (and repairing), where we replace the original values due to detected errors or violation of integrity constraints. We study the computational complexity of problems that regard the selection of cell values in the presence of integrity constraints. More precisely, we focus on functional dependencies and study three problems: (1) deciding whether the constraints can be satisfied by any choice of values, (2) finding a most probable such choice, and (3) calculating the probability of satisfying the constraints. The data complexity of these problems is determined by the combination of the set of functional dependencies and the collection of uncertain attributes. We give full classifications into tractable and intractable complexities for several classes of constraints, including a single dependency, matching constraints, and unary functional dependencies.

Subject Classification

ACM Subject Classification
  • Information systems → Data management systems
Keywords
  • Probabilistic databases
  • attribute-level uncertainty
  • functional dependencies
  • most probable database

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