Catálogo de publicaciones - libros

In this paper, an approach to synthesize correct programs from specifications is presented. The idea is to extract code from definitions appearing in statements which have been mechanically proved with the help of a proof assistant. This approach has been found when proving the correctness of certain Computer Algebra programs (for Algebraic Topology) by using the Isabelle proof assistant. To ease the understanding of our techniques, they are illustrated by means of examples in elementary arithmetic.

In Information Retrieval (IR) systems, the correct representation of a document through an accurate set of index terms is the basis for obtaining a good performance. If we are not able to both extract and weight appropriately the terms which capture the semantics of the text, this shortcoming will have an effect on all the subsequent processing.

The efficiency and effectiveness of the retrieval of documents which are relevant to a certain topic or user query can be improved by means of the clustering of similar documents as well as by introducing parallel strategies. In this paper we explore the use of unsupervised learning, using clustering algorithms based on neural networks, as well as the introduction of , a kind of low-cost parallel architecture, and study the impact on Information Retrieval.

The incorporation of temporal semantic into the traditional data mining techniques has caused the creation of a new area called Temporal Data Mining. This incorporation is especially necessary if we want to extract useful knowledge from dynamic domains, which are time-varying in nature. However, this process is computationally complex, and therefore it poses more challenges on efficient processing that non-temporal techniques. Based in the inter-transactional framework, in [11] we proposed an algorithm named for mining temporal patterns (sequences) from datasets which uses a unique tree-based structure for storing all frequent patterns discovered in the mining process. However, in each data mining process, the algorithm must generate the whole structure from scratch. In this work, we propose an extension which consists in the reusing of structures generated in previous data mining process in order to reduce the execution time of the algorithm.

Most Data Mining tasks are performed by the application of Machine Learning techniques. Metaheuristic approaches are becoming very useful for designing efficient tools in Machine Learning. Metaheuristics are general strategies to design efficient heuristic procedures. Scatter Search is a recent metaheuristic that has been successfully applied to solve standard problems in three central paradigms of Machine Learning: Clustering, Classification and Feature Selection. We describe the main components of the Scatter Search metaheuristic and the characteristics of the specific designs to be applied to solve standard problems in these tasks.

Despite the great success of data mining being applied for personalization in web environments, it has not yet been massively applied in the e-learning domains. In this paper, we outline a web usage mining project which has been initiated in University of Cantabria. The aim of this project is to develop tools which let us improve its Web-based learning environment in two main aspects: the first that the teacher obtains information which allows him to evaluate the learning process and the second that the student feels supported in this task.

Similarity queries in traditional databases work directly on attribute values. But, often similar attribute values do not indicate similar meanings. Semantic background information is needed to enhance similarity query performance. In this paper a method will be addressed which follows the idea to map attribute values to multidimensional points and then interpret the distances between that points as similarity. The second part brings the questions “How to arrange these points that they correspond to real world?” and “Can that be done automatically?” into focus and comes to the following result: For the case that all similarities are known in advance a good solution is given otherwise it turns to a complex optimization problem.

In this paper we present a Web-based CASE tool for hybrid software projects that supports first stages of InSCo, an extended methodology based on CommonKADS and RUP. The tool InSCo Requisite will guide the development of a sort of software where knowledge-based components are integrated with traditional information systems. Furthermore, this tool will allow to manage several development projects at the same time, and determine the users which will take part in each development team.

This paper focuses on improving ACS (Augmentive Communication Systems) by means of an adaptive evolutionary hypermedia. One of the most important features of our approach is the separation of the different aspects involved in the development, use and maintenance of the communication system. Concerning the knowledge representation aspect we use an ontology model that permits specifying the semantic of the represented reality. Regarding the presentation aspect, in order to generate the hypermedia structures we provide a mechanism that allows the creation of different views of the global knowledge model. About the navigation aspect it is important to emphasise its multimodal facet: at the technology level (PC or PDA) and at the interaction level (depending on the access restrictions and the selection units). The user adaptation aspect permits to analyse and to personalise the user navigation using his user profile, his user model and a set of appropriate adaptive methods. In addition, during the whole process we apply an evolutionary mechanism to evolve these aspects in an integral form.

Due to the exponential growth of Internet it is very important to have good knowledge structures that let to obtain good results in Web search. The aim of this work is to discover the user tendencies when they use the search engines and to know the limitations of the knowledge structures that GUMSe uses. With this information is possible to design a more efficient system. For this reason, it is analyzed the set of keywords and queries more frequently used in the search engines and how WordNet manage it. This information is very useful to avoid bad situations in our meta-search engine.