Abstract
Nesting problems are particularly hard combinatorial problems. They involve the positioning of a set of small arbitrarily-shaped pieces on a large stretch of material, without overlapping them. The problem constraints are bidimensional in nature and have to be imposed on each pair of pieces. This all-to-all pattern results in a quadratic number of constraints. Constraint programming has been proven applicable to this category of problems, particularly in what concerns exploring them to optimality. But it is not easy to get effective propagation of the bidimensional constraints represented via finite-domain variables. It is also not easy to achieve incrementality in the search for an improved solution: an available bound on the solution is not effective until very late in the positioning process. In the sequel of work on positioning non-convex polygonal pieces using a CLP model, this work is aimed at improving the expressiveness of constraints for this kind of problems and the effectiveness of their resolution using global constraints. A global constraint "outside" for the non-overlapping constraints at the core of nesting problems has been developed using the constraint programming interface provided by Sicstus Prolog. The global constraint has been applied together with a specialized backtracking mechanism to the resolution of instances of the problem where optimization by Integer Programming techniques is not considered viable. The use of a global constraint for nesting problems is also regarded as a first step in the direction of integrating Integer Programming techniques within a Constraint Programming model.

Abstract
Nesting problems are particularly hard combinatorial problems. They involve the positioning of a set of small arbitrarily-shaped pieces on a large stretch of material, without overlapping them. The problem constraints are bidimensional in nature and have to be imposed on each pair of pieces. This all-to-all pattern results in a quadratic number of constraints. Constraint programming has been proven applicable to this category of problems, particularly in what concerns exploring them to optimality. But it is not easy to get effective propagation of the bidimensional constraints represented via finite-domain variables. It is also not easy to achieve incrementality in the search for an improved solution: an available bound on the solution is not effective until very late in the positioning process. In the sequel of work on positioning non-convex polygonal pieces using a CLP model, this work is aimed at improving the expressiveness of constraints for this kind of problems and the effectiveness of their resolution using global constraints. A global constraint "outside" for the non-overlapping constraints at the core of nesting problems has been developed using the constraint programming interface provided by Sicstus Prolog. The global constraint has been applied together with a specialized backtracking mechanism to the resolution of instances of the problem where optimization by Integer Programming techniques is not considered viable. The use of a global constraint for nesting problems is also regarded as a first step in the direction of integrating Integer Programming techniques within a Constraint Programming model.

Abstract
Geographical Information Systems were originally intended to deal with snapshots representing a single state of some reality but there are more and more applications requiring the representation and querying of time-varying information. This work addresses the representation of moving objects on GIS. The continuous nature of movement raises problems for representation in information systems due to the limited capacity of storage systems and the inherently discrete nature of measurement instruments. The stored information has therefore to be partial and does not allow an exact inference of the real-world object's behavior. To cope with this, query operations must take uncertainty into consideration in their semantics in order to give accurate answers to the users. The paper proposes a set of operations to be included in a GIS or a spatial database to make it able to answer queries on the spatio-temporal behavior of moving objects. The operations have been selected according to the requirements of real applications and their semantics with respect to uncertainty is specified. A collection of examples from a case study is included to illustrate the expressiveness of the proposed operations.

Abstract

Abstract
It is recognized by the Information Retrieval community that context affects the retrieval process. Query formulation and relevance assessment are stages where the user role is central. The first determines what the system will search for and the second is frequently used to evaluate how the system behaved. With a large human involvement, these stages are expected to be largely influenced by user and task characteristics. To analyze the influence of these context features on the specified stages of health information retrieval, we conducted a user study in which we collected user features through two questionnaires. User characteristics include features like age, gender, web search experience, health search experience and familiarity with the medical topic. Task features include the medical specialty, the question type, the task's clarity and the task's easiness. Besides user and task features, the relevance assessment analysis also covered features related to the query and document. We found many variables do indeed affect query formulation and relevance judgment. Some of our results question evaluations using test collections and ask for evaluation models that incorporate other kind of success measures. Copyright 2010 ACM.

Abstract
We have conducted a user study to evaluate several generalist and health-specific search engines on health information retrieval. Users evaluated the relevance of the top 30 documents of 4 search engines in two different health information needs. We introduce the concepts of local and global precision and analyze how they affect the evaluation. Results show that Google surpasses the precision of all other engines, including the health-specific ones, and that precision differs with the type of clinical question and its medical specialty.