Checking query containment with the CQC method

Farré, Carles; Teniente, Ernest; Urpí, Toni

doi:10.1016/j.datak.2004.08.002

Cited by 30 publications

(33 citation statements)

References 40 publications

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“…The CQC E extends the previous Constructive Query Containment (CQC) method (Farré, Teniente, and Urpí 2005) by adding the computation of explanations 2 (i.e., point (2) above). That is the reason why in this paper we focus mainly on this kind of feedback.…”

Section: A Reasoning Engine That Provides Explanations-the Cqc E Methodsmentioning

confidence: 99%

“…The different possible instantiations are determined by the application of a Variable Instantiation Pattern (VIP). A few VIPs were defined by the original CQC method in (Farré, Teniente, and Urpí 2005) but the most common one consists in trying to instantiate a variable with either a fresh constant or a previously used constant. The application of one VIP or another depends on the syntactic properties of the logic expressions.…”

Section: Aux(hs)  Hascompanion(hs P)mentioning

confidence: 99%

“…Figure 12 shows the CQC E -expansion rules used by ExpandNode. The Variable Instantiation Patterns (VIPs) used by expansion rule A2.1 are those defined in (Farré, Teniente, and Urpí 2005). The Normalize function used by rules A3 and B1 returns the normalized version of the given conjunction of literals.…”

Section: Fig 11 Cqc E -Tree Exploration Processmentioning

confidence: 99%

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AuRUS: explaining the validation of UML/OCL conceptual schemas

et al. 2013

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Abstract:The validation and the verification of conceptual schemas have attracted a lot of interest during the last years, and several tools have been developed to automate this process as much as possible. This is achieved, in general, by assessing whether the schema satisfies different kinds of desirable properties which ensure that the schema is correct. In this paper we describe AuRUS, a tool we have developed to analyze UML/OCL conceptual schemas and to explain their (in)correctness. When a property is satisfied, AuRUS provides a sample instantiation of the schema showing a particular situation where the property holds. When it is not, AuRUS provides an explanation for such unsatisfiability, i.e., a set of integrity constraints which is in contradiction with the property.

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Section: A Reasoning Engine That Provides Explanations-the Cqc E Methodsmentioning

confidence: 99%

Section: Aux(hs)  Hascompanion(hs P)mentioning

confidence: 99%

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AuRUS: explaining the validation of UML/OCL conceptual schemas

et al. 2013

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“…Moreover, it is able to determine several properties on a database schema, namely state satisfiability, predicate liveliness, constraint redundancy and state reachability [7,8]. It is a semidecidable procedure for finite satisfiability and unsatisfiability.…”

Section: The Cqc Methods In a Nutshellmentioning

confidence: 99%

Reasoning on UML Class Diagrams with OCL Constraints

Queralt

Teniente

2006

Conceptual Modeling - ER 2006

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Abstract. We propose a new approach to check whether a given UML class diagram with its OCL integrity constraints satisfies a set of desirable properties such as schema satisfiability, class liveliness, redundancy of integrity constraints or reachability of partially specified states. Our approach is based on translating both the class diagram and the OCL constraints into a logic representation. Then, we use the CQC Method to verify whether these properties hold for the given diagram and constraints.

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“…In [52], the authors show how to transform UML class models and a subset of OCL into a constraint-solving problem for the CQC method [26], a semi-decision procedure for the finite satisfiability of deductive database schemes. The authors provide informal definitions for class consistency and weak consistency.…”

Section: Related Workmentioning

confidence: 99%

Efficient analysis of pattern-based constraint specifications

Wahler

Basin

Brucker³

et al. 2009

Softw Syst Model

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Precision and consistency are important prerequisites for class models to conform to their intended domain semantics. Precision can be achieved by augmenting models with design constraints and consistency can be achieved by avoiding contradictory constraints. However, there are different views of what constitutes a contradiction for design constraints. Moreover, state-of-the-art analysis approaches for proving constrained models consistent either scale poorly or require the use of interactive theorem proving. In this paper, we present a heuristic approach for efficiently analyzing constraint specifications built from constraint patterns. This analysis is based on precise notions of consistency for constrained class models and exploits the semantic properties of constraint patterns, thereby enabling syntax-based consistency checking in polynomial-time. We introduce a consistency checker implementing these ideas and we report on case studies in applying our approach to analyze industrial-scale models. These studies show that pattern-based constraint development supports the creation Communicated by Dr. Alessandra Cavarra. M. Wahler ( )

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Checking query containment with the CQC method

Cited by 30 publications

References 40 publications

AuRUS: explaining the validation of UML/OCL conceptual schemas

AuRUS: explaining the validation of UML/OCL conceptual schemas

Reasoning on UML Class Diagrams with OCL Constraints

Efficient analysis of pattern-based constraint specifications

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