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As the important of global data sharing is widely utilized in many corporations, it is well know as multidatabase. However, the system occurs and interesting issue. It is global constraint checking. It is mandatory to set up a potential checking application inside; therefore, global constraint checking needs these following essential characteristics such as 1) mobility 2) heterogeneity and 3) robustness. The effective way to implement the checking is using Aglets which is well recognized as one of the good mobile agent. Aglets is very appropriate because it contains the ability of mobility, and it is 100% Java compatible and open source. In this thesis, we construct the application of global constraint checking following these steps. To begin with starting step, user enters the insert statement. The system then receives the input, and then connection with Global Metadatabase begins. It will optimize the proper route for checking. Its optimized data will be sent out with the mobile agents to the remote sites. Eventually, results will be collected and show to user.

Nous étudions des classes d'automates à états finis calculant sur les arbres, étendus par des contraintes permettant de tester des égalités et diségalités entre sous-arbres. Nous nous concentrons sur des automates d'arbres à contraintes globales où les tests sont opérés en fonction des états que l'automate atteint lors de ses calculs. De tels automates ont été introduit dans le cadre de travaux sur les documents semi-structurés. Nous procédons ici à une comparaison détaillée en expressivité entre ces automates et d'autres modèles permettant de réaliser des tests similaires, comme les automates à contraintes entre frères ou les automates d'arbres avec une mémoire auxiliaire. Nous montrons comment de tels automates peuvent être utilisés pour vérifier des propriétés de sécurité sur les protocoles cryptographiques. Les automates d'arbres ont déjà été utilisés pour modéliser les messages échangés lors d'une session d'un protocole. En ajoutant des contraintes d'égalité, nous pouvons décrire précisement des sessions qui utilisent à plusieurs reprises un même message, évitant ainsi une approximation trop grande. Nous répondons ensuite positivement au problème de la décision du vide des langages reconnus par les automates à contraintes globales. En montrant que leur expressivité est très proche de celle des automates opérant sur des représentations de termes par des graphes orientés acycliques, nous en déduisons une procédure de décision du vide en temps non-déterministe doublement exponentiel. Finalement, nous étudions le problème de la décision du vide pour des automates à contraintes globales pour lesquels on autorise des contraintes dites de clé, exprimant intuitivement que tous les sous arbres d'un certain type dans un arbre en entrée sont distincts deux à deux. Le type des clés est classiquement utilisé pour représenter un identifiant unique, comme un numéro de sécurité sociale.Nous décrivons alors une procédure de décision du vide de complexité non-élementaire. Nous montrons que cette procédure est très robuste, et qu'il est possible d'étendre les automates avec des contraintes supplémentaires, comme des contraintes de comptage ou des tests locaux, tout en préservant la décidabilité du vide. / We study here several classes of finite state automata running on trees, extended with constraints that allow to test for equalities or disequalities between subterms. We focus on tree automata with global constraints where the tests are done depending on the states reached by the automaton on its runs. Such automata were introduced in studies on semi-structured documents. We do here a detailed comparison between those automata and other models that allow to operate similar tests, like tree automata with constraints between brothers, or tree automata with an auxiliary memory. We show how such automata may be used to verify security properties on cryptographic protocols. Tree automata have already been used to modelize the messages exchanged during a protocol session. By adding equality constraints, we can describe precisely protocol sessions that use a same message several times, hence avoiding an approximation. Then, we answer positively the decision problem of the emptiness of the languages recognized by tree automata with global constraints. By showing that their expressivity is very close from the one of the automata operating on directed acyclic graphs representations of terms, we infer an emptiness decision procedure in double exponential non-deterministic time. Finally, we study the emptiness decision problem for automata with global constraints where we authorize "key constraints", that intuitively allow that all subtrees of a given type in an input tree are distincts. We give an emptiness decision procedure of non-primitive recursive complexity. Key constraints are classicaly used to represent a unique identifier. We describe a non-primitive recusrive emptiness decision procedure. We show that this procedure is very robust and that it allows to extend the automata with additionnal constraints, like counting constraints or local tests, while preserving decidability.

<p>Many problems in business, industry, and academia can be modelled as constraint programs consisting of matrices of decision variables. Such “matrix models” often have symmetry. In particular, they often have row and column symmetry as the rows and columns can freely be permuted without affecting the satisfiability of assignments. Existing methods have difficulty in dealing with the super-exponential number of symmetries in a problem with row and column symmetry. We therefore propose some ordering constraints which can effectively break such symmetries. To use these constraints in practice, we develop some efficient linear time propagators. We demonstrate the effectiveness of these symmetry breaking ordering constraints on a wide range of problems. We also show how such ordering constraints can be used to deal with partial symmetries, as well as value symmetries.</p>

Many problems in business, industry, and academia can be modelled as constraint programs consisting of matrices of decision variables. Such “matrix models” often have symmetry. In particular, they often have row and column symmetry as the rows and columns can freely be permuted without affecting the satisfiability of assignments. Existing methods have difficulty in dealing with the super-exponential number of symmetries in a problem with row and column symmetry. We therefore propose some ordering constraints which can effectively break such symmetries. To use these constraints in practice, we develop some efficient linear time propagators. We demonstrate the effectiveness of these symmetry breaking ordering constraints on a wide range of problems. We also show how such ordering constraints can be used to deal with partial symmetries, as well as value symmetries.

A wide range of problems can be modelled as constraint satisfaction problems (CSPs), that is, a set of constraints that must be satisfied simultaneously. Constraints can either be represented extensionally, by explicitly listing allowed combinations of values, or intensionally, whether by an equation, propositional logic formula, or other means. Intensionally represented constraints, known as global constraints, are a powerful modelling technique, and many modern CSP solvers provide them. We give examples to show how problems that deal with product configuration can be modelled with such constraints, and how this approach relates to other modelling formalisms. The complexity of CSPs with extensionally represented constraints is well understood, and there are several known techniques that can be used to identify tractable classes of such problems. For CSPs with global constraints, however, many of these techniques fail, and far fewer tractable classes are known. In order to remedy this state of affairs, we undertake a systematic review of research into the tractability of CSPs. In particular, we look at CSPs with extensionally represented constraints in order to understand why many of the techniques that give tractable classes for this case fail for CSPs with global constraints. The above investigation leads to two discoveries. First, many restrictions on how the constraints of a CSP interact implicitly rely on a property of extensionally represented constraints to guarantee tractability. We identify this property as being a bound on the number of solutions in key parts of the instance, and find classes of global constraints that also possess this property. For such classes, we show that many known tractability results apply. Furthermore, global constraints allow us to treat entire CSP instances as constraints. We combine this observation with the above result, and obtain new tractable classes of CSPs by dividing a CSP into smaller CSPs drawn from known tractable classes. Second, for CSPs that simply do not possess the above property, we look at how the constraints of an instance overlap, and how assignments to the overlapping parts extend to the rest of the problem. We show that assignments that extend in the same way can be identified. Combined with a new structural restriction, this observation leads to a second set of tractable classes. We conclude with a summary, as well as some observations about potential for future work in this area.

La programmation par contraintes est une approche intéressante pour traiter des problèmes d’ordonnancement. En ordonnancement cumulatif, les activités sont définies par leur date de début, leur durée et la quantité de ressource nécessaire à leur exécution. La ressource totale disponible (la capacité) en chaque instant est fixe. La contrainte globale Cumulative modélise ce problème en programmation par contraintes. Dans de nombreux cas pratiques, la date limite de fin d’un projet est fixée et ne peut être retardée. Dans ce cas, il n’est pas toujours possible de trouver un ordonnancement des activités qui n’engendre aucun dépassement de la capacité en ressource. On peut alors tolérer de relâcher la contrainte de capacité, dans une limite raisonnable, pour obtenir une solution. Nous proposons une nouvelle contrainte globale : la contrainte SoftCumulative qui étend la contrainte Cumulative pour prendre en compte ces dépassements de capacité. Nous illustrons son pouvoir de modélisation sur plusieurs problèmes pratiques, et présentons différents algorithmes de filtrage. Nous adaptons notamment les algorithmes de balayage et d’Edge-Finding à la contrainte SoftCumulative. Nous montrons également que certains problèmes pratiques nécessitent d’imposer des violations de capacité pour satisfaire des règles métiers, modélisées par des contraintes additionnelles. Nous présentons une procédure de filtrage originale pour traiter ces dépassements imposés. Nous complétons notre étude par une approche par décomposition. Enfin, nous testons et validons nos différentes techniques de résolution par une série d’expériences. / Constraint programming is an interesting approach to solve scheduling problems. In cumulative scheduling, activities are defined by their starting date, their duration and the amount of resource necessary for their execution. The total available resource at each point in time (the capacity) is fixed. In constraint programming, the Cumulative global constraint models this problem. In several practical cases, the deadline of theproject is fixed and can not be delayed. In this case, it is not always possible to find a schedule that does not lead to an overload of the resource capacity. It can be tolerated to relax the capacity constraint, in a reasonable limit, to obtain a solution. We propose a new global constraint : the SoftCumulative constraint that extends the Cumulative constraint to handle these overloads. We illustrate its modeling power on several practical problems, and we present various filtering algorithms. In particular, we adapt the sweep and Edge-Finding algorithms to the SoftCumulative constraint. We also show that some practical problems require to impose overloads to satisfy business rules, modelled by additional constraints. We present an original filtering procedure to deal with these imposed overloads. We complete our study by an approach by decomposition. At last, we test and validate our different resolution techniques through a series of experiments.

We study in this thesis three well known global constraints. The All-Different constraint restricts a set of variables to be assigned to distinct values. The <em>global cardinality constraint</em> (GCC) ensures that a value <em>v</em> is assigned to at least <em>l_v</em> variables and to at most <em>u_v</em> variables among a set of given variables where <em>l_v</em> and <em>u_v</em> are non-negative integers such that <em>l_v</em> &le; <em>u_v</em>. The Inter-Distance constraint ensures that all variables, among a set of variables <em>x</em>₁, . . . , <em>x_n</em>, are pairwise distant from <em>p</em>, i. e. |<em>x_i</em> - <em>x_j</em>| &ge; <em>p</em> for all <em>i</em> &ne; <em>j</em>. The All-Different constraint, the GCC, and the Inter-Distance constraint are largely used in scheduling problems. For instance, in scheduling problems where tasks with unit processing time compete for a single resource, we have an All-Different constraint on the starting time variables. When there are <em>k</em> resources, we have a GCC with <em>l_v</em> = 0 and <em>u_v</em> = <em>k</em> over all starting time variables. Finally, if tasks have processing time <em>t</em> and compete for a single resource, we have an Inter-Distance constraint with <em>p</em> = <em>t</em> over all starting time variables. We present new propagators for the All-Different constraint, the GCC, and the Inter-Distance constraint i. e. , new filtering algorithms that reduce the search space according to these constraints. For a given consistency, our propagators outperform previous propagators both in practice and in theory. The gains in performance are achieved through judicious use of advanced data structures combined with novel results on the structural properties of the constraints.

We study in this thesis three well known global constraints. The All-Different constraint restricts a set of variables to be assigned to distinct values. The <em>global cardinality constraint</em> (GCC) ensures that a value <em>v</em> is assigned to at least <em>l_v</em> variables and to at most <em>u_v</em> variables among a set of given variables where <em>l_v</em> and <em>u_v</em> are non-negative integers such that <em>l_v</em> &le; <em>u_v</em>. The Inter-Distance constraint ensures that all variables, among a set of variables <em>x</em>₁, . . . , <em>x_n</em>, are pairwise distant from <em>p</em>, i. e. |<em>x_i</em> - <em>x_j</em>| &ge; <em>p</em> for all <em>i</em> &ne; <em>j</em>. The All-Different constraint, the GCC, and the Inter-Distance constraint are largely used in scheduling problems. For instance, in scheduling problems where tasks with unit processing time compete for a single resource, we have an All-Different constraint on the starting time variables. When there are <em>k</em> resources, we have a GCC with <em>l_v</em> = 0 and <em>u_v</em> = <em>k</em> over all starting time variables. Finally, if tasks have processing time <em>t</em> and compete for a single resource, we have an Inter-Distance constraint with <em>p</em> = <em>t</em> over all starting time variables. We present new propagators for the All-Different constraint, the GCC, and the Inter-Distance constraint i. e. , new filtering algorithms that reduce the search space according to these constraints. For a given consistency, our propagators outperform previous propagators both in practice and in theory. The gains in performance are achieved through judicious use of advanced data structures combined with novel results on the structural properties of the constraints.

Diese Arbeit befasst sich mit der Entwicklung einer Applikation, welche Infrastrukturdaten über Eisenbahnnetze generiert. Dabei bildet die Erzeugung der topologischen Informationen den Schwerpunkt dieser Arbeit. Der Anwender charakterisiert hierfür vorab das gewünschte Eisenbahnnetz, wobei die geforderten Eigenschaften die Randbedingungen darstellen, die bei der Synthese zu beachten sind. Zur Einhaltung dieser Bedingungen wird die Constraint-Programmierung eingesetzt, welche durch ihr spezielles Programmierparadigma konsistente Lösungen effizient erzeugt. Dies wird u.a. durch die Nachnutzung so genannter globaler Constraints erreicht. Aus diesem Grund wird insbesondere auf den Einsatz der Constraint-Programmierung bei der Modellierung und Implementierung der Applikation eingegangen. / This work deals with the development of an application, which generates infrastructure data of railway networks. The focus of this work concentrates on the generation process of topological information. As input for the application a characterization of the intended railway network is given as attributes, which are handled as constraints in the generation process. To satisfy these restrictions constraint programming, a special programming paradigm, which is able to search efficently consistent solutions, is applied. In particular, the use of so-called global constraints improves the computation. For that reason the role of constraint-programming in modelling and implementing these application is discussed in more detail.