Most of the schema matching tools are assembled from multiple match algorithms, each employing a particular technique to improve matching accuracy and making matching systems extensible and customizable to a specific domain. Recently, it has been pointed out that the main issue is how to select the most suitable match algorithms to execute for a given domain and how to adjust the multiple parameters. The solutions provided by current schema matching tools consist in aggregating the results obtained by several match algorithms to improve the quality of the discovered matches. In this article, we present a novel method to replace this aggregation function and its drawbacks. Unlike other composite matchers, our matching engine makes use of a decision tree to combine the most appropriate match algorithms. As a first consequence, the performance of the system is improved since only a subset of match algorithms from a large library is used. The second advantage is the improvement of the quality of matches. Indeed, for a given domain, only the most suitable match algorithms are used. Our approach is also able to learn the most appropriate match algorithms for a given domain by relying on the expert feedback. It can also selftune some parameters like thresholds and the performance versus quality ratio.

View materialization is a commonly used technique in many data-intensive systems to improve the query performance. Increasing need for large-scale data processing has led to investigating the view selection problem in distributed complex scenarios where a set of cooperating computer nodes may share data and issue numerous queries. In our work, the view selection and data placement problem is studied given a limited amount of resources e.g. storage space capacity per computer node and maximum view maintenance cost. We also consider the IO and CPU costs for each computer node as well as the network bandwidth. To address this problem, we have proposed a constraint programming approach which is based on constraint reasoning to tackle problems that aim to satisfy a set of constraints. Then, we have designed a set of efficient heuristics that result in a drastic reduction in the solution space so that the problem becomes solvable for complex scenarios consisting of realistically large numbers of sites, queries and views. Our experimental study shows that our approach performs consistently better compared to a practical approach designed for large-scale distributed environments which uses a genetic algorithm to compute which view has to be materialized at what computer node.

Traditional pattern mining algorithms generally suffer from a lack of flexibility. In this paper, we propose a SAT formulation of the problem to successfully mine frequent flexible sequences occurring in transactional datasets. Our SAT-based approach can easily be extended with extra constraints to address a broad range of pattern mining applications. To demonstrate this claim, we formulate and add several constraints, such as gap and span constraints, to our model in order to extract more specific patterns. We also use interactive solving to perform important derived tasks, such as closed pattern mining or maximal pattern mining. Finally, we prove the practical feasibility of our SAT model by running experiments on two real datasets.

This paper presents a new interval-based operator for continuous constrained global optimization. It is built upon a new filtering operator, named TEC, which constructs a bounded subtree using a Branch and Contract process and returns the parallel-to-axes hull of the leaf domains/boxes. Two extensions of TEC use the information contained in the leaf boxes of the TEC subtree to improve the two bounds of the objective function value: (i) for the lower bounding, a polyhedral hull of the (projected) leaf boxes replaces the parallel-to-axes hull, (ii) for the upper bounding, a good feasible point is searched for inside a leaf box of the TEC subtree, following a look-ahead principle. The algorithm proposed is an auto-adaptive version of TEC that plays with both extensions according to the outcomes of the upper and lower bounding phases. Experimental results show a significant speed-up on several state-of-the-art instances.

Preference reasoning is a central problem in decision support. There exist various ways to interpret a set of qualitative preferences. Conditional preference logics allow to deal with semantics such as optimistic, pessimistic, strong or not. In this paper, we study the complexity of the main problems in optimistic/pessimistic preference logic: undominated, consistency and dominance. We show that they are all NP-hard in general, with some becoming polynomial under specific semantics. Our second contribution is to show that the dominance problem, which has an online component in its definition, is compilable to polynomial time.

The automated generation of models that conform to a given meta-model is an important challenge in Model Driven Engineering, as well for model transformation testing, as for designing and exploring new meta-models. Amongst the main issues, we are mainly concerned by scalability, flexibility and a reasonable computing time. This paper presents an approach for model generation, which relies on Constraint Programming. After the translation of a meta-model into a CSP, our software generates models that conform to this meta-model, using a Constraint Solver. Our model also includes the most frequent types of OCL constraints. Since we are concerned by the relevance of the produced models, we describe a first attempt to improve them. We outperform the existing approaches from the mentioned point of view, and propose a configurable, easy-to-use and free-access tool, together with an on-line demonstrator.

Assemblage consists in blending base wines in order to create target wines. Recent developments in aroma analysis allow us to measure chemical compounds impacting the taste of wines. This chemical analysis makes it possible to design a decision tool for the following problem: given a set of target wines, determine which volumes must be extracted from each base wine to produce wines that satisfy constraints on aroma concentration, volumes, alcohol contents and price. This paper describes the modeling of wine assemblage as a mixed constrained optimization problem, where the main goal is to minimize the gap to the desired concentrations for every aromatic criterion. The deterministic branch and bound solvers Couenne and IbexOpt behave well on the wine blending problem thanks to their interval constraint propagation/programming and polyhedral relaxation methods. We also study the performance of other optimization goals that could be embedded in a configuration tool, where the different possible interactions amount to solving the same constraints with different objective functions. We finally show on a recent generic wine blending instance that the proposed optimization process scales up well with the number of base wines.

We study how to find a solution to a constraint problem without modeling it. Constraint acquisition systems such as Conacq or ModelSeeker are not able to solve a single instance of a problem because they require positive examples to learn. The recent QuAcq algorithm for constraint acquisition does not require positive examples to learn a constraint network. It is thus able to solve a constraint problem without modeling it: we simply exit from QuAcq as soon as a complete example is classified as positive by the user. In this paper, we propose ASK&SOLVE, an elicitation-based solver that tries to find the best trade off between learning and solving to converge as soon as possible on a solution. We propose several strategies to speed-up ASK&SOLVE. Finally we give an experimental evaluation that shows that our approach improves the state of the art.

This paper presents a simple and fast approach for genome scaffolding, combining constraint modeling and simple graph manipulation. We model the scaffolding problem as an optimization problem on a graph built from a paired-end reads alignment on contigs, then describe an heuristic to solve this problem with the iterative combination of local constraints solving and cycle breaking phases. We tested our approach on a benchmark of various genomes, and compared it with several usual scaffolders. The proposed method is quick, flexible, and provides results comparable to other scaffolders in terms of quality. In addition, contrarily to state of the art approaches that require dedicated servers, it can be run on a basic notebook computer even for large genomes.

Constraint acquisition assists a non-expert user in modeling her problem as a constraint network. In existing constraint acquisition systems the user is only asked to answer very basic questions. The drawback is that when no background knowledge is provided, the user may need to answer a great number of such questions to learn all the constraints. In this paper, we introduce the concept of generalization query based on an aggregation of variables into types. We present a constraint generalization algorithm that can be plugged into any constraint acquisition system. We propose several strategies to make our approach more efficient in terms of number of queries. Finally we experimentally compare the recent QUACQ system to an extended version boosted by the use of our generalization functionality. The results show that the extended version dramatically improves the basic QUACQ.

Nous apprenons des reseaux de contraintes en utilisant des requetes partielles. Autrement dit, nous demandons a l’utilisateur de classer une affectation de sous-ensembles de variables comme positive ou negative. Nous fournissons un algorithme qui, etant donne un exemple complet negatif, apprend une contrainte du reseau cible avec un certain nombre de requetes partielles, logarithmique en taille de l’exemple. Nous presentons une etude theorique sur les bornes inferieures en termes de requetes pour apprendre certaines classes de reseaux de contraintes et montrons l’optimalite de notre algorithme generique dans certains cas. Enfin, nous evaluons experimentalement notre algorithme.

proposons permet d’obtenir un bon compromis entre la capacit´e d’une coh´erence locale `a supprimer des valeurs et le coˆut en temps pour atteindre ce niveau de coh´erence dans un r´eseau de contraintes. Nous validons notre approche sur diff´erents probl`emes de la derni`ere comp´etition des solveurs de contraintes.

View selection is important in many data-intensive systems e.g., commercial database and datawarehousing systems. Given a database (or a data warehouse) schema and a query workload, view selection is to choose an appropriate set of views to be materialized that optimizes the total query cost, given a limited amount of resource, e.g., storage space and total view maintenance cost. The view selection problem is known to be a NP-complete problem. In this paper, we propose a declarative approach that involves a constraint programming technique which is known to be efficient for the resolution of NP-complete problems. The originality of our approach is that it provides a clear separation between formulation and resolution of the problem. For this purpose, the view selection problem is modeled as a constraint satisfaction problem in an easy and declarative way. Then, its resolution is performed automatically by the constraint solver. Furthermore, our approach is flexible and extensible, in that it can easily model and handle new constraints and new heuristic search strategies to reduce the solution space. The performance results show that our approach outperforms the genetic algorithm which is known to provide the best trade-off between quality of solutions in terms of cost saving and execution time.

Working with open data sources can yield high value information but raises major problems in terms of metadata extraction, data source integration and visualization. In this paper we describe a demonstration of WebSmatch, a flexible environment for Web data integration, based on a real, end-to-end data integration scenario over public data from Data Publica. The demonstration focuses on poorly structured input data sources (XLS files).

State-of-the-art constraint solvers uniformly maintain the same level of local consistency (usually arc consistency) on all the instances. We propose parameterized local consistency, an original approach to adjust the level of consistency depending on the instance and on which part of the instance we propagate. We do not use as parameter one of the features of the instance, as done for instance in portfolios of solvers. We use as parameter the stability of values, which is a feature based on the state of the arc consistency algorithm during its execution. Parameterized local consistencies choose to enforce arc consistency or a higher level of local consistency on a value depending on whether the stability of the value is above or below a given threshold. We also propose a way to dynamically adapt the parameter, and thus the level of local consistency, during search. This approach allows us to get a good trade-off between the number of values pruned and the computational cost. We validate our approach on various problems from the CSP competition.

Assemblage consists in blending base wines in order to create a target wine. Recent developments in aroma analysis allow us to measure chemical compounds impacting the taste of wines. This chemical analysis makes it possible to design a decision tool for the following problem: given a set of target wines, determine which volumes must be extracted from each base wine to produce wines that satisfy constraints on aroma concentration, volumes, alcohol contents and price. This paper describes the modeling of wine assemblage as a non linear constrained Min-Max problem (minimizing the gap to the desired concentrations for every aromatic criterion) efficiently handled by the Ibex interval branch and bound.

This work is a contribution of Artificial Intelligence to Software Engineering. We present a comprehensive approach to metamodel instantiation using CSP. The generation of models which conform to a given metamodel is a crucial issue in Software Engineering, especially when it comes to produce a variate and large dataset of relevant models to test model transformations or to properly design new metamodels. We define an original constraint modeling of the problem of generating a model conform to a metamodel, also taking into account its additional OCL constraints. The generation process we describe appears to be quicker, more efficient and flexible than any other state-of-the-art approach.

We learn constraint networks by asking the user partial queries. That is, we ask the user to classify assignments to subsets of the variables as positive or negative. We provide an algorithm that, given a negative example, focuses onto a constraint of the target network in a number of queries logarithmic in the size of the example. We give information theoretic lower bounds for learning some simple classes of constraint networks and show that our generic algorithm is optimal in some cases. Finally we evaluate our algorithm on some benchmarks.

This paper presents an approach for comparing study programmes in automatic way by using two software tools: (i) the IKAS system allowing construction of concepts maps representing curricula and their transformation into ontologies and (ii) the WebSmatch tool allowing matching of the ontologies and providing visualization of comparison results. The results of our experiment on a real dataset of study programmes in some European universities show the effectiveness of the approach.

En oenologie, l'assemblage consiste a m elanger plusieurs cuv ees a fin d'obtenir un vin cible. Des progr es dans l'analyse des aromes permettent aujourd'hui de mesurer un ensemble de compos es chimiques influen cant le gout d'un vin. Il devient ainsi possible de concevoir un outil d'aide a la d ecision pour le probl eme suivant : etant donn e un ensemble de vins cibles a produire, quelles quantit es doit-on pr elever dans chaque cuv ee afin de produire des vins qui respectent des contraintes de concentration en aromes, de volumes, de degr e alcoolique et de prix. L'article d ecrit la mod elisation de ce probl eme sous forme d'une optimisation Min-Max (pour minimiser les ecarts obtenus avec les concentrations souhait ees pour chaque crit ere aromatique) sous contraintes num eriques lin eaires et quadratiques, et son traitement effi cace avec le branch and bound a intervalles de Ibex.