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ConjunctiveQuery Containment and Constraint Satisfaction
 Journal of Computer and System Sciences
, 1998
"... Conjunctivequery containment is recognized as a fundamental problem in database query evaluation and optimization. At the same time, constraint satisfaction is recognized as a fundamental problem in artificial intelligence. What do conjunctivequery containment and constraint satisfaction have in c ..."
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Cited by 168 (14 self)
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Conjunctivequery containment is recognized as a fundamental problem in database query evaluation and optimization. At the same time, constraint satisfaction is recognized as a fundamental problem in artificial intelligence. What do conjunctivequery containment and constraint satisfaction have in common? Our main conceptual contribution in this paper is to point out that, despite their very different formulation, conjunctivequery containment and constraint satisfaction are essentially the same problem. The reason is that they can be recast as the following fundamental algebraic problem: given two finite relational structures A and B, is there a homomorphism h : A ! B? As formulated above, the homomorphism problem is uniform in the sense that both relational structures A and B are part of the input. By fixing the structure B, one obtains the following nonuniform problem: given a finite relational structure A, is there a homomorphism h : A ! B? In general, nonuniform tractability results do not uniformize. Thus, it is natural to ask: which tractable cases of nonuniform tractability results for constraint satisfaction and conjunctivequery containment do uniformize? Our main technical contribution in this paper is to show that several cases of tractable nonuniform constraint satisfaction problems do indeed uniformize. We exhibit three nonuniform tractability results that uniformize and, thus, give rise to polynomialtime solvable cases of constraint satisfaction and conjunctivequery containment.
Decomposing Constraint Satisfaction Problems Using Database Techniques
, 1994
"... There is a very close relationship between constraint satisfaction problems and the satisfaction of joindependencies in a relational database which is due to a common underlying structure, namely a hypergraph. By making that relationship explicit we are able to adapt techniques previously developed ..."
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Cited by 95 (24 self)
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There is a very close relationship between constraint satisfaction problems and the satisfaction of joindependencies in a relational database which is due to a common underlying structure, namely a hypergraph. By making that relationship explicit we are able to adapt techniques previously developed for the study of relational databases to obtain new results for constraint satisfaction problems. In particular, we prove that a constraint satisfaction problem may be decomposed into a number of subproblems precisely when the corresponding hypergraph satisfies a simple condition. We show that combining this decomposition approach with existing algorithms can lead to a significant improvement in efficiency.
Structure Identification in Relational Data
, 1997
"... This paper presents several investigations into the prospects for identifying meaningful structures in empirical data, namely, structures permitting effective organization of the data to meet requirements of future queries. We propose a general framework whereby the notion of identifiability is give ..."
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Cited by 81 (2 self)
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This paper presents several investigations into the prospects for identifying meaningful structures in empirical data, namely, structures permitting effective organization of the data to meet requirements of future queries. We propose a general framework whereby the notion of identifiability is given a precise formal definition similar to that of learnability. Using this framework, we then explore if a tractable procedure exists for deciding whether a given relation is decomposable into a constraint network or a CNF theory with desirable topology and, if the answer is positive, identifying the desired decomposition. Finally, we
Identifying Independencies in Causal Graphs with Feedback
 In Uncertainty in Artificial Intelligence: Proceedings of the Twelfth Conference
, 1996
"... We show that the dseparation criterion constitutes a valid test for conditional independence relationships that are induced by feedback systems involving discrete variables. 1 INTRODUCTION It is well known that the dseparation test is sound and complete relative to the independencies assumed in t ..."
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Cited by 24 (0 self)
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We show that the dseparation criterion constitutes a valid test for conditional independence relationships that are induced by feedback systems involving discrete variables. 1 INTRODUCTION It is well known that the dseparation test is sound and complete relative to the independencies assumed in the construction of Bayesian networks [Verma and Pearl, 1988, Geiger et al., 1990]. In other words, any dseparation condition in the network corresponds to a genuine independence condition in the underlying probability distribution and, conversely, every dconnection corresponds to a dependency in at least one distribution compatible with the network. The situation with feedback systems is more complicated, primarily because the probability distributions associated with such systems do not lend themselves to a simple product decomposition. The joint distribution of feedback systems cannot be written as a product of the conditional distributions of each child variable, given its parents. Rath...
Unifying clustertree decompositions for reasoning in graphical models
 Artificial Intelligence
, 2005
"... The paper provides a unifying perspective of treedecomposition algorithms appearing in various automated reasoning areas such as jointree clustering for constraintsatisfaction and the cliquetree algorithm for probabilistic reasoning. Within this framework, we introduce a new algorithm, called bu ..."
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Cited by 21 (10 self)
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The paper provides a unifying perspective of treedecomposition algorithms appearing in various automated reasoning areas such as jointree clustering for constraintsatisfaction and the cliquetree algorithm for probabilistic reasoning. Within this framework, we introduce a new algorithm, called buckettree elimination (BT E), that extends Bucket Elimination (BE) to trees, and show that it can provide a speedup of n over BE for various reasoning tasks. Timespace tradeoffs of treedecomposition processing are analyzed. 1
Tree Decompositions with Applications to Constraint Processing
, 1990
"... This paper concerns the task of removing redundant information from a given knowledge base, and restructuring it in the form of a tree, so as to admit efficient problem solving routines. We offer a novel approach which guarantees the removal of all redundancies that hide a tree structure. We develo ..."
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Cited by 12 (3 self)
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This paper concerns the task of removing redundant information from a given knowledge base, and restructuring it in the form of a tree, so as to admit efficient problem solving routines. We offer a novel approach which guarantees the removal of all redundancies that hide a tree structure. We develop a polynomial time algorithm that, given an arbitrary constraint network, generates a precise tree representation whenever such a tree can be extracted from the input network, otherwise, the fact that no tree representation exists is acknowledged, and the tree generated may serve as a good approximation to the original network. I.
A Randomised Schema Mutator for Evolutionary Database Optimisation
 The Australian Computer Journal
, 1993
"... In this paper we focus on randomised evolutionary optimisation. We introduce a general framework for the optimisation of data models, based on the concept of evolution. This evolution is guided by a randomised schema mutator. Although our approach is expressed in terms of database optimisation, our ..."
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Cited by 10 (6 self)
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In this paper we focus on randomised evolutionary optimisation. We introduce a general framework for the optimisation of data models, based on the concept of evolution. This evolution is guided by a randomised schema mutator. Although our approach is expressed in terms of database optimisation, our ideas are applicable to other fields of randomised evolutionary optimisation of computer models, especially when similar (graph structured) models are used. Keywords and phrases: global optimisation, evolutionary optimisation, adaptive search, randomised algorithms, conceptual data models, transformation of data models, database optimisation. 1 Introduction 1.1 Intention of the paper In this paper we describe the underlying algorithm of a Prototype Evolutionary Database Optimiser, under development at the Department of Information Systems, University of Nijmegen, The Netherlands. The paper has three main contributions. Firstly, we introduce a formal framework for nondeterministic evolution...
Decompositions of partially defined boolean functions
 DISCRETE APPLIED MATHEMATICS
, 1995
"... The problem of recognizing decomposability of incompletely defined Boolean relations is considered. The results include polynomial time algorithms for certain important types of decompositions, as well as NPcompleteness proofs for more complex structures. ..."
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Cited by 8 (6 self)
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The problem of recognizing decomposability of incompletely defined Boolean relations is considered. The results include polynomial time algorithms for certain important types of decompositions, as well as NPcompleteness proofs for more complex structures.
Automated Database Schema Design Using Mined Data Dependencies
 J. Amer. Soc. Inform. Sci
, 1998
"... Data dependencies are used in database schema design to enforce the correctness of a database as well as to reduce redundant data. These dependencies are usually determined from the semantics of the attributes and are then enforced upon the relations. This paper describes a bottomup procedure for d ..."
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Cited by 7 (0 self)
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Data dependencies are used in database schema design to enforce the correctness of a database as well as to reduce redundant data. These dependencies are usually determined from the semantics of the attributes and are then enforced upon the relations. This paper describes a bottomup procedure for discovering multivalued dependencies (MVDs) in observed data without knowing `a priori the relationships amongst the attributes. The proposed algorithm is an application of the technique we designed for learning conditional independencies in probabilistic reasoning. A prototype system for automated database schema design has been implemented. Experiments were carried out to demonstrate both the effectiveness and efficiency of our method. 1