Inconsistency-Tolerant Database Repairs and Simplified Repair Checking by Measure-Based Integrity Checking

Decker, Hendrik

doi:10.1007/978-3-662-55947-5_7

Cited by 3 publications

(2 citation statements)

References 45 publications

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“…This makes it possible to apply all the results about inconsistency measures for propositional knowledge bases to a wide range of applications. We believe that this is a significant advance for the whole idea of inconsistency measurement that has been developed for propositional knowledge bases, and explored in several other individual settings such as software specifications [46], databases [4,14,44,51], and ontologies [63,64], among others.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

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

Grant

Parisi

2021

Ann Math Artif Intell

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“…Proposed definitions, examples, and (dis-)advantages might originate from other software engineering domains. In the database community, for example, there has been long-term research on maintaining and restoring consistency, and performing operations in the presence of errors (Decker & Martinenghi 2011;Decker 2017). Consequently, to avoid reinventing the wheel, research from related fields should be taken into account, as long as it matches the problem domain and transferring results to MDE is possible.…”

Section: Introductionmentioning

confidence: 99%

Tolerance in Model-Driven Engineering: A Systematic Literature Review with Model-Driven Tool Support

Weidmann¹,

Kannan²,

Anjorin³

2021

Preprint

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Managing models in a consistent manner is an important task in the field of Model-Driven Engineering (MDE).Although restoring and maintaining consistency is desired in general, recent work has pointed out that always strictly enforcing consistency at any point of time is often not feasible in real-world scenarios, and sometimes even contrary to what a user expects from a trustworthy MDE tool. The challenge of tolerating inconsistencies has been discussed from different viewpoints within and outside the modelling community, but there exists no structured overview of existing and current work in this regard.In this paper, we provide such an overview to help join forces tackling the unresolved problems of tolerating inconsistencies in MDE. We follow the standard process of a Systematic Literature Review (SLR) to point out what tolerance means, how it relates to uncertainty, which examples for tolerant software systems have already been discussed, and which benefits and drawbacks tolerating inconsistencies entails. Furthermore, we propose a tool-chain that helps conducting SLRs in computer science and also eases the reproduction of results. Relevant meta-data of the collected sources is uniformly described in a textual modelling language and exported to the graph database Neo4j to query aggregated information.

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On measuring inconsistency in definite and indefinite databases with denial constraints

Parisi

Grant

2023

Artificial Intelligence

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Inconsistency-Tolerant Database Repairs and Simplified Repair Checking by Measure-Based Integrity Checking

Cited by 3 publications

References 45 publications

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

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

Tolerance in Model-Driven Engineering: A Systematic Literature Review with Model-Driven Tool Support

On measuring inconsistency in definite and indefinite databases with denial constraints

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