An ASP System with Functions, Lists, and Sets

Calimeri, Francesco; Cozza, Susanna; Ianni, Giovambattista; Leone, Nicola

doi:10.1007/978-3-642-04238-6_46

Cited by 22 publications

(25 citation statements)

References 6 publications

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“…We can use extensions of ASP with set-and numerical aggregation to build the contingency set associated to a cause, e.g. the DLV system [29] by means of its DLV-Complex extension [16,17] that supports set membership and union as built-ins. We introduce a binary predicate preCont to hold a cause (id) and a possibly non-maximal set of elements from its contingency set, and the following rules:…”

Section: Specifying Tuple-based Causesmentioning

confidence: 99%

“…In this section we show in detail how the examples and repairs-programs extended with causality elements of Section 4 can be specified and executed in the DLV-Complex system [16,17].…”

Section: Examples Of Tuple-based Causes With Dlv-complexmentioning

confidence: 99%

“…However, if we accept minimal repairs that also satisfy ψ (when D already did so), then the only minimal repair is D = D {τ 1 , τ 4 , τ 8 }. 17 It is non-monotonic in that its violation view, which captures the tuples that violate it, is defined by a non-monotonic query. Monotonic ICs, i.e.…”

Section: Causes Under Integrity Constraintsmentioning

confidence: 99%

“…4. We show several examples of ASP and their execution with the DLV and DLV-Complex systems [29,16,17]. 5.…”

mentioning

confidence: 99%

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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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Section: Specifying Tuple-based Causesmentioning

confidence: 99%

“…In this section we show in detail how the examples and repairs-programs extended with causality elements of Section 4 can be specified and executed in the DLV-Complex system [16,17].…”

Section: Examples Of Tuple-based Causes With Dlv-complexmentioning

confidence: 99%

Section: Causes Under Integrity Constraintsmentioning

confidence: 99%

“…4. We show several examples of ASP and their execution with the DLV and DLV-Complex systems [29,16,17]. 5.…”

mentioning

confidence: 99%

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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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“…Actually, for each instance of this case of Swoosh, it is possible to construct nondisjunctive stratified ASP, Π UC (D), that uses function and set terms [29], for set union and set membership. Such a program has a single stable model that corresponds to the unique resolved instance guaranteed by Swoosh, and it can be computed in polynomial time in data.…”

Section: Mds and Swooshmentioning

confidence: 99%

Generic and Declarative Approaches to Data Quality Management

Bertossi

Bravo

2013

Handbook of Data Quality

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Data quality assessment and data cleaning tasks have traditionally been addressed through procedural solutions. Most of the time, those solutions have been applicable to specific problems and domains. In the last few years we have seen the emergence of more generic solutions; and also of declarative and rule-based specifications of the intended solutions of data cleaning processes. In this chapter we review some of those historical and recent developments.

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Repair-Based Degrees of Database Inconsistency

Bertossi¹

2019

Lecture Notes in Computer Science

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We propose a generic numerical measure of the inconsistency of a database with respect to a set of integrity constraints. It is based on an abstract repair semantics. In particular, an inconsistency measure associated to cardinalityrepairs is investigated in detail. More specifically, it is shown that it can be computed via answer-set programs, but sometimes its computation can be intractable in data complexity. However, polynomial-time deterministic and randomized approximations are exhibited. The behavior of this measure under small updates is analyzed, obtaining fixed-parameter tractability results. Furthermore, alternative inconsistency measures are proposed and discussed.

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An ASP System with Functions, Lists, and Sets

Cited by 22 publications

References 6 publications

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

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

Generic and Declarative Approaches to Data Quality Management

Repair-Based Degrees of Database Inconsistency

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