Catálogo de publicaciones - revistas

<jats:p>Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence. We introduce the reader to the motivations for solving the ambiguity of words and provide a description of the task. We overview supervised, unsupervised, and knowledge-based approaches. The assessment of WSD systems is discussed in the context of the Senseval/Semeval campaigns, aiming at the objective evaluation of systems participating in several different disambiguation tasks. Finally, applications, open problems, and future directions are discussed.</jats:p>

<jats:p>Formal methods and testing are two important approaches that assist in the development of high-quality software. While traditionally these approaches have been seen as rivals, in recent years a new consensus has developed in which they are seen as complementary. This article reviews the state of the art regarding ways in which the presence of a formal specification can be used to assist testing.</jats:p>

<jats:p> In practice, any database management system sometimes needs reorganization, that is, a change in some aspect of the logical and/or physical arrangement of a database. In traditional practice, many types of reorganization have required denying access to a database (taking the database offline) during reorganization. Taking a database offline can be unacceptable for a highly available (24-hour) database, for example, a database serving electronic commerce or armed forces, or for a very large database. A solution is to reorganize online (concurrently with usage of the database, incrementally during users' activities, or interpretively). This article is a tutorial and survey on requirements, issues, and strategies for online reorganization. It analyzes the issues and then presents the strategies, which use the issues. The issues, most of which involve design trade-offs, include use of partitions, the locus of control for the process that reorganizes (a background process or users' activities), reorganization by copying to newly allocated storage (as opposed to reorganizing in place), use of differential files, references to data that has moved, performance, and activation of reorganization. The article surveys online strategies in three categories of reorganization. The first category, maintenance, involves restoring the physical arrangement of data instances without changing the database definition. This category includes restoration of clustering, reorganization of an index, rebalancing of parallel or distributed data, garbage collection for persistent storage, and cleaning (reclamation of space) in a log-structured file system. The second category involves changing the physical database definition; topics include construction of indexes, conversion between B <jats:sup>+</jats:sup> -trees and linear hash files, and redefinition (e.g., splitting) of partitions. The third category involves changing the logical database definition. Some examples are changing a column's data type, changing the inheritance hierarchy of object classes, and changing a relationship from one-to-many to many-to-many. The survey encompasses both research and commercial implementations, and this article points out several open research topics. As highly available or very large databases continue to become more common and more important in the world economy, the importance of online reorganization is likely to continue growing. </jats:p>

<jats:p>Web clustering engines organize search results by topic, thus offering a complementary view to the flat-ranked list returned by conventional search engines. In this survey, we discuss the issues that must be addressed in the development of a Web clustering engine, including acquisition and preprocessing of search results, their clustering and visualization. Search results clustering, the core of the system, has specific requirements that cannot be addressed by classical clustering algorithms. We emphasize the role played by the quality of the cluster labels as opposed to optimizing only the clustering structure. We highlight the main characteristics of a number of existing Web clustering engines and also discuss how to evaluate their retrieval performance. Some directions for future research are finally presented.</jats:p>

<jats:p>The literature provides a wide range of techniques to assess and improve the quality of data. Due to the diversity and complexity of these techniques, research has recently focused on defining methodologies that help the selection, customization, and application of data quality assessment and improvement techniques. The goal of this article is to provide a systematic and comparative description of such methodologies. Methodologies are compared along several dimensions, including the methodological phases and steps, the strategies and techniques, the data quality dimensions, the types of data, and, finally, the types of information systems addressed by each methodology. The article concludes with a summary description of each methodology.</jats:p>

<jats:p>Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.</jats:p>

<jats:p>Formal methods use mathematical models for analysis and verification at any part of the program life-cycle. We describe the state of the art in the industrial use of formal methods, concentrating on their increasing use at the earlier stages of specification and design. We do this by reporting on a new survey of industrial use, comparing the situation in 2009 with the most significant surveys carried out over the last 20 years. We describe some of the highlights of our survey by presenting a series of industrial projects, and we draw some observations from these surveys and records of experience. Based on this, we discuss the issues surrounding the industrial adoption of formal methods. Finally, we look to the future and describe the development of a Verified Software Repository, part of the worldwide Verified Software Initiative. We introduce the initial projects being used to populate the repository, and describe the challenges they address.</jats:p>

<jats:p>We survey recent progress in software model checking.</jats:p>

<jats:p>Automated deduction uses computation to perform symbolic logical reasoning. It has been a core technology for program verification from the very beginning. Satisfiability solvers for propositional and first-order logic significantly automate the task of deductive program verification. We introduce some of the basic deduction techniques used in software and hardware verification and outline the theoretical and engineering issues in building deductive verification tools. Beyond verification, deduction techniques can also be used to support a variety of applications including planning, program optimization, and program synthesis.</jats:p>