Measuring and Computing Database Inconsistency via Repairs

Bertossi, Leopoldo

doi:10.1007/978-3-030-00461-3_26

Cited by 12 publications

(14 citation statements)

References 16 publications

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“…To formalize "repair-based" inconsistency measures, we first need to define repairs for ST databases, inspired by the approach for probabilistic spatio-temporal databases by Parisi and Grant (2017), and then define an inconsistency measure as the minimum cost of a repair-a similar idea has been recently considered by Bertossi (2018) to define inconsistency measures for relational databases, where the issues concerning spatio-temporal data, as well as postulates satisfaction, are not considered.…”

Section: Repair-based Inconsistency Measuresmentioning

confidence: 99%

Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases

Grant¹,

Martínez²,

Molinaro

et al. 2021

jair

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The problem of managing spatio-temporal data arises in many applications, such as location-based services, environmental monitoring, geographic information systems, and many others. Often spatio-temporal data arising from such applications turn out to be inconsistent, i.e., representing an impossible situation in the real world. Though several inconsistency measures have been proposed to quantify in a principled way inconsistency in propositional knowledge bases, little effort has been done so far on inconsistency measures tailored for the spatio-temporal setting. In this paper, we define and investigate new measures that are particularly suitable for dealing with inconsistent spatio-temporal information, because they explicitly take into account the spatial and temporal dimensions, as well as the dimension concerning the identifiers of the monitored objects. Specifically, we first define natural measures that look at individual dimensions (time, space, and objects), and then propose measures based on the notion of a repair. We then analyze their behavior w.r.t. common postulates defined for classical propositional knowledge bases, and find that the latter are not suitable for spatio-temporal databases, in that the proposed inconsistency measures do not often satisfy them. In light of this, we argue that also postulates should explicitly take into account the spatial, temporal, and object dimensions and thus define “dimension-aware” counterparts of common postulates, which are indeed often satisfied by the new inconsistency measures. Finally, we study the complexity of the proposed inconsistency measures.

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Section: Repair-based Inconsistency Measuresmentioning

confidence: 99%

Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases

Grant¹,

Martínez²,

Molinaro

et al. 2021

jair

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“…8. I mv (K ex ) = 3 5 as 3 of the 5 atoms appear in problematic formulas. There have been several proposals to classify inconsistency measures.…”

Section: # (K Ex ) =mentioning

confidence: 99%

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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“…A similar approach based on abstract repair semantics was taken in [12,13] in order to introduce an abstract inconsistency measure of a database w.r.t. a set on ICs.…”

Section: Abstract Causes From Abstract Repairsmentioning

confidence: 99%

Characterizing and Computing Causes for Query Answers in Databases from Database Repairs and Repair Programs

Bertossi

2018

Lecture Notes in Computer Science

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A correspondence between database tuples as causes for query answers in databases and tuple-based repairs of inconsistent databases with respect to denial constraints has already been established. In this work, answerset programs that specify repairs of databases are used as a basis for solving computational and reasoning problems about causes. Here, causes are also introduced at the attribute level by appealing to a both null-based and attributebased repair semantics. The corresponding repair programs are presented, and they are used as a basis for computation and reasoning about attribute-level causes. They are extended to deal with the case of causality under integrity constraints. Several examples with the DLV system are shown.

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Measuring and Computing Database Inconsistency via Repairs

Cited by 12 publications

References 16 publications

Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases

Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases

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

Characterizing and Computing Causes for Query Answers in Databases from Database Repairs and Repair Programs

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