Repair-Based Degrees of Database Inconsistency

Bertossi, Leopoldo

doi:10.1007/978-3-030-20528-7_15

Cited by 20 publications

(23 citation statements)

References 48 publications

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“…Starting from the pioneering work of [5], there has been a wealth of research on the problem of consistent query answering in inconsistent databases [7,39,36,23,8]. Most of the existing works make a distinction between consistent and inconsistent answers based on the notion of a database repair (see [11,10] for a survey). A repair is a database instance obtained by applying some minimal changes on the initial database instance in order to make it compliant with a given set of integrity constraints.…”

Section: Related Workmentioning

confidence: 99%

“…Usually, there exist several possible repairs of a given database and for such a reason, following the certain answers semantics, consistent answers to a given query are defined as being the intersection of the query answers on all possible repairs of the initial database instance. The problem of consistent query answering has been investigated in several settings depending on: (i) the definition of repairs, where a repair is defined in terms of the sets of inserted and/or deleted tuples [11,10,34,13], or updates [39,32,6] and (ii) the considered integrity constraints and the query language. The latter has been studied for a variety of queries and constraints such as first-order queries and binary universal constraints or single functional dependencies [5], union of conjunctive queries and key and inclusion dependencies [14], first-order queries and denial constraints [16], to mention a few.…”

Section: Related Workmentioning

confidence: 99%

“…The ultimate goal is, however, to provide measures to quantify the inconsistency of a KBs in order, for example, to allow comparison of full KBs based on their inconsistency degrees. Extensions of such an approach to the database field has been recently investigated in [10] where inconsistency measures based on various classes of repair semantics are studied. However, their notion of inconsistency measures (despite the name) relies on restoring the consistency of a database, thus on the class of repairs admitted by the database.…”

Section: Related Workmentioning

confidence: 99%

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Evaluating top-k queries with inconsistency degrees

2020

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We study the problem of augmenting relational tuples with inconsistency awareness and tackling top-k queries under a set of denial constraints (DCs). We define a notion of inconsistent tuples with respect to a set of DCs and define two measures of inconsistency degrees, which consider single and multiple violations of constraints. In order to compute these measures, we leverage two models of provenance, namely why-provenance and provenance polynomials. We investigate top-k queries that allow to rank the answer tuples by their inconsistency degrees. Since one of our measure is monotonic and the other non-monotonic, we design an integrated top-k algorithm to compute the top-k results of a query w.r.t. both inconsistency measures. By means of an extensive experimental study, we gauge the effectiveness of inconsistency-aware query answering and the efficiency of our algorithm with respect to a baseline, where query results are fully computed and ranked afterwards.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Evaluating top-k queries with inconsistency degrees

2020

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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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“…Hence, measuring database inconsistency has been revisited and generalized recently by data management community. [9] argued that both the admissible repair actions and how close we want stay to the instance at hand should be taken into account when defining such measure. To achieve this, database repairs [8] could be applied to define degrees of inconsistency.…”

Section: Introductionmentioning

confidence: 99%

Complexity and Efficient Algorithms for Data Inconsistency Evaluating and Repairing

Miao¹,

Cai²,

Li³

et al. 2020

Preprint

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Data inconsistency evaluating and repairing are major concerns in data quality management. As the basic computing task, optimal subset repair is not only applied for cost estimation during the progress of database repairing, but also directly used to derive the evaluation of database inconsistency. Computing an optimal subset repair is to find a minimum tuple set from an inconsistent database whose remove results in a consistent subset left. Tight bound on the complexity and efficient algorithms are still unknown. In this paper, we improve the existing complexity and algorithmic results, together with a fast estimation on the size of optimal subset repair. We first strengthen the dichotomy for optimal subset repair computation problem, we show that it is not only APXcomplete, but also NPhard to approximate an optimal subset repair with a factor better than 17/16 for most cases. We second show a (2 − 0.5 σ−1 )approximation whenever given σ functional dependencies, and a (2 − η k + η k k )-approximation when an η k -portion of tuples have the k-quasi-Turán property for some k > 1. We finally show a sublinear estimator on the size of optimal Srepair for subset queries, it outputs an estimation of a ratio 2n + n with a high probability, thus deriving an estimation of FD-inconsistency degree of a ratio 2 + . To support a variety of subset queries for FD-inconsistency evaluation, we unify them as the ⊆-oracle which can answer membership-query, and return p tuples uniformly sampled whenever given a number p. Experiments are conducted on * Supported by NSFC xxxx, NSFC xxxx range queries as an implementation of ⊆-oracle, and results show the efficiency of our FD-inconsistency degree estimator.

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Repair-Based Degrees of Database Inconsistency

Cited by 20 publications

References 48 publications

Evaluating top-k queries with inconsistency degrees

Evaluating top-k queries with inconsistency degrees

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

Complexity and Efficient Algorithms for Data Inconsistency Evaluating and Repairing

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