Dealing with Inconsistency in Databases: An Overview

Caroprese, Luciano; Fazzinga, Bettina; Flesca, Sergio; Furfaro, Filippo; Greco, Sergio; Molinaro, Cristian; Parisi, Francesco; Pugliese, Andrea; Saccà, Domenico; Trubitsyna, Irina; Zumpano, Ester

doi:10.1007/978-3-319-61893-7_9

Cited by 2 publications

(3 citation statements)

References 33 publications

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“…-I C (S) = 3 as there must be at least 3 atoms, for example a 8 , a 6 , and a 3 that must be given the value B for a 3-valued interpretation in order to satisfy all the formulas. -I mv (S) = 8 16 = 1 2 as 8 of the 16 atoms are in problematic formulas.…”

Section: The Calculation Of the Inconsistency Measuresmentioning

confidence: 99%

“…As inconsistency in real-world information systems can not be easily avoided, many inconsistency-tolerant approaches have been developed to live with inconsistency [21], and provide appropriate mechanisms to handle inconsistent data [5,8,18]. For instance, approaches for dealing with inconsistency in databases include consistent query answering frameworks [2,9], data repairing [1,39,41,43], interactive data repairing and cleaning systems (e.g.…”

Section: Theoremmentioning

confidence: 99%

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General information spaces: measuring inconsistency, rationality postulates, and complexity

Grant

Parisi

2021

Ann Math Artif Intell

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AI systems often need to deal with inconsistent information. For this reason, since the early 2000s, some AI researchers have developed ways to measure the amount of inconsistency in a knowledge base. By now there is a substantial amount of research about various aspects of inconsistency measuring. The problem is that most of this work applies only to knowledge bases formulated as sets of formulas in propositional logic. Hence this work is not really applicable to the way that information is actually stored. The purpose of this paper is to extend inconsistency measuring to real world information. We first define the concept of general information space which encompasses various types of databases and scenarios in AI systems. Then, we show how to transform any general information space to an inconsistency equivalent propositional knowledge base, and finally apply propositional inconsistency measures to find the inconsistency of the general information space. Our method allows for the direct comparison of the inconsistency of different information spaces, even though the data is presented in different ways. We demonstrate the transformation on four general information spaces: a relational database, a graph database, a spatio-temporal database, and a Blocks world scenario, where we apply several inconsistency measures after performing the transformation. Then we review so-called rationality postulates that have been developed for propositional knowledge bases as a way to judge the intuitive properties of these measures. We show that although general information spaces may be nonmonotonic, there is a way to transform the postulates so they can be applied to general information spaces and we show which of the measures satisfy which of the postulates. Finally, we discuss the complexity of inconsistency measures for general information spaces.

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Section: The Calculation Of the Inconsistency Measuresmentioning

confidence: 99%

Section: Theoremmentioning

confidence: 99%

General information spaces: measuring inconsistency, rationality postulates, and complexity

Grant

Parisi

2021

Ann Math Artif Intell

Self Cite

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show abstract

“…There is a growing number of applications where inconsistent information arises, often because data are obtained from multiple sources [3]. This has led to an extensive body of work on handling inconsistent data, and in particular inconsistent databases.…”

Section: Introductionmentioning

confidence: 99%

Inconsistency Measures for Relational Databases

Parisi,

Grant

2019

Preprint

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In this paper, building on work done on measuring inconsistency in knowledge bases, we introduce inconsistency measures for databases. In particular, focusing on databases with denial constraints, we first consider the natural approach of virtually transforming a database into a propositional knowledge base and then applying well-known measures. However, using this method, tuples and constraints are equally considered in charge of inconsistencies. Then, we introduce a version of inconsistency measures blaming database tuples only, i.e., treating integrity constraints as irrefutable statements. We analyze the compliance of database inconsistency measures with standard rationality postulates and find interesting relationships between measures. Finally, we investigate the complexity of the inconsistency measurement problem as well as of the problems of deciding whether the inconsistency is lower than, greater than, or equal to a given threshold.

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Dealing with Inconsistency in Databases: An Overview

Cited by 2 publications

References 33 publications

General information spaces: measuring inconsistency, rationality postulates, and complexity

General information spaces: measuring inconsistency, rationality postulates, and complexity

Inconsistency Measures for Relational Databases

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