Among learning characteristics for L2 learners, language learning strategies are one characteristic that has the potential of being influenced by language instruction. This report attempts to review the most salient research and taxonomies for LLS to provide a comprehensive overview for those who would like to teach, learn, or conduct more research in the field. It records various definitions that have been assigned to LLS and traces the history of LLS research that has accumulated over the past thirty years. It also reviews empirical research that has been conducted by applying certain taxonomies to find relationships between other learner characteristics. Finally, it looks into ways that LLS can be applied to the four language skills: reading, listening, speaking, and writing, and discusses research designed to analyze the effectiveness of Strategy Based Instruction for the specific skills. The concluding section finds particular avenues for further research and application of LLS. / text

This thesis considers the tasks involved in exploratory analysis of temporal graph data, and the visual techniques which are able to support these tasks. There has been an enormous increase in the amount and availability of graph (network) data, and in particular, graph data that is changing over time. Understanding the mechanisms involved in temporal change in a graph is of interest to a wide range of disciplines. While the application domain may differ, many of the underlying questions regarding the properties of the graph and mechanism of change are the same. The research area of temporal graph visualisation seeks to address the challenges involved in visually representing change in a graph over time. While most graph visualisation tools focus on static networks, recent research has been directed toward the development of temporal visualisation systems. By representing data using computer-generated graphical forms, Information Visualisation techniques harness human perceptual capabilities to recognise patterns, spot anomalies and outliers, and find relationships within the data. Interacting with these graphical representations allow individuals to explore large datasets and gain further insightinto the relationships between different aspects of the data. Visual approaches are particularly relevant for Exploratory Data Analysis (EDA), where the person performing the analysis may be unfamiliar with the data set, and their goal is to make new discoveries and gain insight through its exploration. However, designing visual systems for EDA can be difficult, as the tasks which a person may wish to carry out during their analysis are not always known at outset. Identifying and understanding the tasks involved in such a process has given rise to a number of task taxonomies which seek to elucidate the tasks and structure them in a useful way. While task taxonomies for static graph analysis exist, no suitable temporal graph taxonomy has yet been developed. The first part of this thesis focusses on the development of such a taxonomy. Through the extension and instantiation of an existing formal task framework for general EDA, a task taxonomy and a task design space are developed specifically for exploration of temporal graph data. The resultant task framework is evaluated with respect to extant classifications and is shown to address a number of deficiencies in task coverage in existing works. Its usefulness in both the design and evaluation processes is also demonstrated. Much research currently surrounds the development of systems and techniques for visual exploration of temporal graphs, but little is known about how the different types of techniques relate to one another and which tasks they are able to support. The second part of this thesis focusses on the possibilities in this area: a design spaceof the possible visual encodings for temporal graph data is developed, and extant techniques are classified into this space, revealing potential combinations of encodings which have not yet been employed. These may prove interesting opportunities for further research and the development of novel techniques. The third part of this work addresses the need to understand the types of analysis the different visual techniques support, and indeed whether new techniques are required. The techniques which are able to support the different task dimensions are considered. This task-technique mapping reveals that visual exploration of temporalgraph data requires techniques not only from temporal graph visualisation, but also from static graph visualisation and comparison, and temporal visualisation. A number of tasks which are unsupported or less-well supported, which could prove interesting opportunities for future research, are identified. The taxonomies, design spaces, and mappings in this work bring order to the range of potential tasks of interest when exploring temporal graph data and the assortmentof techniques developed to visualise this type of data, and are designed to be of use in both the design and evaluation of temporal graph visualisation systems.

No description available.

La BI et le cloud computing sont deux grands sujets de recherche en informatique et en système d’information en particulier. Une recherche combinant ces deux concepts est d'un intérêt double : D’une part, dans les entreprises, la BI devient de plus en plus une partie importante du système d'information qui nécessite des investissements en termes de performances de calcul et des volumes de données. D’autre part, le cloud computing offre de nouvelles opportunités pour gérer les données à des fins d’analyse.Etant donné les possibilités de cloud, la question de la migration de l'ensemble du système d’information y compris la BI est d'un grand intérêt. En particulier, les chercheurs doivent fournir aux professionnels des modèles et des méthodes qui puissent les aider à migrer vers le cloud.Que faire pour que la BI puisse fournir aux managers un service de mise à disposition de données d’analyse au travers du cloud ? La question de recherche est : Comment aider les organisations à migrer leur BI vers le cloud ?Dans cette thèse, nous répondons à cette question en utilisant l'approche science de conception (design science). Nous mettons en place une aide à la décision de la migration de la BI vers le cloud qui s'appuie sur les taxonomies. Nous proposons un modèle de guidage opérationnel qui est instancié par une taxonomie de la BI dans le cloud et dont découlent les règles pour la migration de la BI vers le cloud. / BI and cloud computing are two major areas of computer science research and in particular in information system. A research combining these two concepts has a double interest : On the one hand, in business, the BI becomes increasingly an important part of the information system which requires investment in terms of computing performance and data volumes. On the other hand, cloud computing offers new opportunities to manage data for analysis.Given the possibilities of cloud, migration question of the information system including BI is of great interest. In particular, researchers must provide models and methods to help professional in BI migration to the cloud.The research question is : how to migrate BI to the cloud?In this thesis, we address this issue using design science research approach. We implement a decision-making help for BI migration to the cloud based on taxonomies. We provide an operational guidance model that is instantiated by a BI taxonomy in the cloud and from that rules for BI migration to the cloud are arised.

Essa pesquisa tem como foco da investigação as classificações dos índios Wajãpi do Amapari (AP) sobre as plantas cultivadas, denominadas na língua nativa de temitãgwerã. A descrição e análise das formas de classificação das temitãgwerã são feitas a partir de dois grandes arcabouços teóricos: de um lado os estudos sobre taxonomias nativas, empreendidos pelo viés da antropologia cognitiva; de outro as proposições sobre um pensamento ameríndio, empreendidas pela etnologia propriamente. Essas duas linhas teóricas são convocadas a dialogar uma vez que se objetiva demonstrar que as classificações não são elaborações isoladas do pensamento, nem são elementos exclusivamente abstratos e intelectuais, mas fazem parte da experiência cotidianamente vivenciada. Assim, busca-se evidenciar as relações entre alguns sistemas de classificação wajãpi e aspectos cosmológicos, aspectos sociais, formas de transmissão de conhecimentos e formas de manejo agrícola. / The focal point of this research is the study of the systems that the Wajãpi Indians from Amapari (AP -Brazil) utilize to classify the plants that they cultivate, which are known as temitãgwerã in their language. The descriptions and analyses of these folk taxonomies are made with the support of two theoretical frameworks: from one hand the studies of folk taxonomies from a cognitive anthropology perspective and, from the other hand, taking into account the propositions about the Amerindian thought derived from the ethnology itself. In fact, these two theoretical lines should complement each other once it is intended to demonstrate that taxonomies are not isolated from others aspects of thought, neither are exclusively abstract or intellectual elements, but part of the experiences of the daily life. Therefore the major goal of this investigation is to show that folk taxonomies keep relations with cosmology aspects, sociology aspects, manners of knowledge transmission and agricultural management.

Mineração de Dados é um processo de natureza iterativa e interativa responsável por identificar padrões em grandes conjuntos de dados, objetivando extrair conhecimento válido, útil e inovador a partir desses. Em Mineração de Dados, Regras de Associação é uma técnica que consiste na identificação de padrões intrínsecos ao conjunto de dados. Essa técnica tem despertado grande interesse nos pesquisadores de Mineração de Dados e nas organizações, entretanto, a mesma possui o inconveniente de gerar grande volume de conhecimento no formato de regras, dificultando a análise e interpretação dos resultados pelo usuário. Nesse contexto, este trabalho tem como objetivo principal generalizar e eliminar Regras de Associação não interessantes e/ou redundantes, facilitando, dessa maneira, a análise das regras obtidas com relação à compreensibilidade e tamanho do conjunto de regras. A generalização das Regras de Associação é realizada com o uso de taxonomias. Entre os principais resultados deste trabalho destacam-se a proposta e a implementação do algoritmo GART e do módulo computacional RulEE-GAR. O algoritmo GART (Generalization of Association Rules using Taxonomies - Generalização de Regras de Associação usando Taxonomias) utiliza taxonomias para generalizar Regras de Associação. Já o módulo RulEE-GAR, além de facilitar o uso do algoritmo GART durante a identificação de taxonomias e generalização de regras, provê funcionalidades para analisar as Regras de Associação generalizadas. Os experimentos realizados, neste trabalho, mostraram que o uso de taxonomias na generalização de Regras de Associação pode reduzir o volume de um conjunto de regras. / Data Mining refers to the process of finding patterns in large data sets. The Association Rules in Data Mining try to identify intrinsic behaviors of the data set. This has motivated researchers of Data Mining and organizations. However, the Association Rules have the inconvenient of generating a great amount of knowledge in the form of rules. This makes the analysis and interpretation of the results difficult for the user. Taking this into account, the main objective of this research is the generalization and elimination of non-interesting and/or redundant Association Rules. This facilite the analysis of the rules with respect to the compreensibility and the size of the rule set. The generalization is realized using taxonomies. The main results of this research are the proposal and the implementation of the algorithm GART and of the computational module RulEE-GAR. The algorithm GART (Generalization of Association Rules using Taxonomies) uses taxonomies to generalize Association Rules. The module RulEE-GAR facilitates the use of the algorithm GART in the identification of taxonomies and generalization of rules and provide functionalities to the analysis of the generalized Association Rules. The results of experiments showed that the employment of taxonomies in the generalization of Association Rules can reduce the size of a rule set.

Para o sucesso do processo de mineração de dados é importante que o conhecimento extraí?do seja compreensível e interessante para que o usuário final possa utilizá-lo em um sistema inteligente ou em processos de tomada de decisão. Um grande problema, porém, é identificado quando a tarefa de mineração de dados denominada associação é utilizada: a geração de um grande volume de regras. Taxonomias podem ser utilizadas para facilitar a análise e interpretação das regras de associação, uma vez que as mesmas provêm uma visão de como os itens podem ser hierarquicamente classificados. Em função dessa hierarquia é possível obter regras mais gerais que representem um conjunto de itens. Dentro desse contexto, neste trabalho é apresentada uma metodologia para construção semi-automática de taxonomias, que inclui procedimentos automáticos e interativos para a realização dessa tarefa. Essa combinação possibilita a utilização do conhecimento do especialista e também o auxilia na identificação de grupos. Entre os principais resultados deste trabalho, pode-se destacar a proposta e implementação do algoritmo SACT (Semi-automatic Construction of Taxonomies - Construção Semi-automática de Taxonomias), que provê a utilização da metodologia proposta. Para viabilizar a utilização do algoritmo, foi desenvolvido o módulo computacional RulEESACT. Com o objetivo de viabilizar e analisar a qualidade da metodologia proposta e do módulo desenvolvido, foi realizado um estudo de caso no qual foram construída taxonomias para duas bases de dados utilizando o RulEE-SACT. Uma das taxonomias foi analisada e validada por uma especialista do domínio. Posteriormente, as taxonomias e as bases de transações foram fornecidas para dois algoritmos de generalização de regras de associação a fim de analisar a aplicação das taxonomias geradas / I n the data mining process it is important that the extracted knowledge is understandable and interesting to the final user, so it can be used to support in the decision making. However, the data mining task named association has one problem: it generates a big volume of rules. Taxonomies can be used to facilitate the analysis and interpretation of association rules, because they provide an hierarchical vision of the items. This hierarchy enables the obtainment of more general rules, which represent a set of items. In this context, a methodology to semi-automatically construct taxonomies is proposed in this work. This methodology includes automatic and interactives procedures in order to construct the taxonomies, using the specialist?s knowledge and also assisting in the identification of groups. One of the main results of this work is the proposal and implementation of the SACT (Semi-automatic Construction of Taxonomies) algorithm, which provides the use of the proposed methodology. In order to facilitate the use of this algorithm, a computational module named RulEE-SACT was developed. Aiming to analyze the viability and quality of the proposed methodology and the developed module, a case study was done. In this case study, taxonomies of two databases were constructed using the RulEE-SACT. One of them was analyzed and validated by a domain specialist. Then the taxonomies and the databases were supplied to two algorithms which generalize association rules, aiming to analyze the use of the generated taxonomies

Mineração de Dados é um processo de natureza iterativa e interativa responsável por identificar padrões em grandes conjuntos de dados, objetivando extrair conhecimento válido, útil e inovador a partir desses. Em Mineração de Dados, Regras de Associação é uma técnica que consiste na identificação de padrões intrínsecos ao conjunto de dados. Essa técnica tem despertado grande interesse nos pesquisadores de Mineração de Dados e nas organizações, entretanto, a mesma possui o inconveniente de gerar grande volume de conhecimento no formato de regras, dificultando a análise e interpretação dos resultados pelo usuário. Nesse contexto, este trabalho tem como objetivo principal generalizar e eliminar Regras de Associação não interessantes e/ou redundantes, facilitando, dessa maneira, a análise das regras obtidas com relação à compreensibilidade e tamanho do conjunto de regras. A generalização das Regras de Associação é realizada com o uso de taxonomias. Entre os principais resultados deste trabalho destacam-se a proposta e a implementação do algoritmo GART e do módulo computacional RulEE-GAR. O algoritmo GART (Generalization of Association Rules using Taxonomies - Generalização de Regras de Associação usando Taxonomias) utiliza taxonomias para generalizar Regras de Associação. Já o módulo RulEE-GAR, além de facilitar o uso do algoritmo GART durante a identificação de taxonomias e generalização de regras, provê funcionalidades para analisar as Regras de Associação generalizadas. Os experimentos realizados, neste trabalho, mostraram que o uso de taxonomias na generalização de Regras de Associação pode reduzir o volume de um conjunto de regras. / Data Mining refers to the process of finding patterns in large data sets. The Association Rules in Data Mining try to identify intrinsic behaviors of the data set. This has motivated researchers of Data Mining and organizations. However, the Association Rules have the inconvenient of generating a great amount of knowledge in the form of rules. This makes the analysis and interpretation of the results difficult for the user. Taking this into account, the main objective of this research is the generalization and elimination of non-interesting and/or redundant Association Rules. This facilite the analysis of the rules with respect to the compreensibility and the size of the rule set. The generalization is realized using taxonomies. The main results of this research are the proposal and the implementation of the algorithm GART and of the computational module RulEE-GAR. The algorithm GART (Generalization of Association Rules using Taxonomies) uses taxonomies to generalize Association Rules. The module RulEE-GAR facilitates the use of the algorithm GART in the identification of taxonomies and generalization of rules and provide functionalities to the analysis of the generalized Association Rules. The results of experiments showed that the employment of taxonomies in the generalization of Association Rules can reduce the size of a rule set.

Dentre as técnicas de mineração de dados encontra-se a associação, a qual identifica todas as associações intrínsecas contidas na base de dados. Entretanto, essa característica, vantajosa por um lado, faz com que um grande número de padrões seja gerado, sendo que muito deles, mesmo sendo estatisticamente aceitos, são triviais, falsos, ou irrelevantes à aplicação. Além disso, a técnica de associação tradicional gera padrões compostos apenas por itens contidos na base de dados, o que leva à extração, em geral, de um conhecimento muito específico. Essa especificidade dificulta a obtenção de uma visão geral do domínio pelos usuários finais, que visam a utilização/exploração de conhecimentos úteis e compreensíveis. Assim, o pós-processamento das regras descobertas se torna um importante tópico, uma vez que há a necessidade de se validar as regras obtidas. Diante do exposto, este trabalho apresenta uma abordagem de pós-processamento de regras de associação que utiliza conhecimento de domínio, expresso via taxonomias, para obter um conjunto de regras de associação generalizadas compacto e representativo. Além disso, a fim de avaliar a representatividade de padrões generalizados, é apresentado também neste trabalho um estudo referente à utilização de medidas de interesse objetivas quando aplicadas a regras de associação generalizadas. Nesse estudo, a semântica da generalização é levada em consideração, já que cada uma delas fornece uma visão distinta do domínio. Como resultados desta tese, foi possível observar que: um conjunto de regras de associação pode ser compactado na presença de um conjunto de taxonomias; para cada uma das semânticas de generalização existe um conjunto de medidas mais apropriado para ser utilizado na avaliação de regras generalizadas / The association technique, one of the data mining techniques, identifies all the intrinsic associations in database. This characteristic, which can be advantageous on the one hand, generates a large number of patterns. Many of these patterns, even statistically accepted, are trivial, spurious, or irrelevant to the application. In addition, the association technique generates patterns composed only by items in database, which in general implies a very specific knowledge. This specificity makes it difficult to obtain a general view of the domain by the final users, who aims the utilization/exploration of useful and comprehensible knowledge . Thus, the post-processing of the discovered rules becomes an important topic, since it is necessary to validate the obtained rules. In this context, this work presents an approach for post-processing association rules that uses domain knowledge, expressed by taxonomies, to obtain a reduced and representative generalized association rule set. In addition, in order to evaluate the representativeness of generalized patterns, a study referent to the use of objective interest measures when applied to generalized association rules is presented. In this study, the generalization semantics is considered, since each semantic provides a distinct view of the domain. As results of this thesis, it was possible to observe that: an association rule set can be compacted with a taxonomy set; for each generalization semantic there is a measure set that is more appropriate to be used in the generalized rules evaluation

El proceso de construir software a partir del ensamblaje e integración de soluciones de software pre-fabricadas, conocidas como componentes COTS (Comercial-Off-The-Shelf) se ha convertido en una necesidad estratégica en una amplia variedad de áreas de aplicación. En general, los componentes COTS son componentes de software que proveen una funcionalidad específica, que están disponibles en el mercado para ser adquiridos e integrados dentro de otros sistemas de software. Los beneficios potenciales de esta tecnología son principalmente la reducción de costes y el acortamiento del tiempo de desarrollo, a la vez que fomenta la calidad. Sin embargo, numerosos retos que van desde problemas técnicos y legales deben ser afrontados para adaptar las actividades tradicionales de ingeniería de software para explotar los beneficios del uso de COTS para el desarrollo de sistemas.Actualmente, existe un incrementalmente enorme mercado de componentes COTS; así, una de las actividades más críticas en el desarrollo de sistemas basados en COTS es la selección de componentes que deben ser integrados en el sistema a desarrollar. La selección está básicamente compuesta de dos procesos principales: La búsqueda de componentes candidatos en el mercado y su posterior evaluación con respecto a los requisitos del sistema. Desafortunadamente, la mayoría de los métodos existentes para seleccionar COTS, se enfocan en el proceso de evaluación, dejando de lado el problema de buscar los componentes en el mercado. La búsqueda de componentes en el mercado no es una tarea trivial, teniendo que afrontar varias características del mercado de COTS, tales como su naturaleza dispersa y siempre creciente, cambio y evolución constante; en este contexto, la obtención de información de calidad acerca de los componentes no es una tarea fácil. Como consecuencia, el proceso de selección de COTS se ve seriamente dañado. Además, las alternativas tradicionales de reuso también carecen de soluciones apropiadas para reusar componentes COTS y el conocimiento adquirido en cada proceso de selección. Esta carencia de propuestas es un problema muy serio que incrementa los riesgos de los proyectos de selección de COTS, además de hacerlos ineficientes y altamente costosos. Esta disertación presenta el método GOThIC (Goal- Oriented Taxonomy and reuse Infrastructure Construction) enfocado a la construcción de infraestructuras de reuso para facilitar la búsqueda y reuso de componentes COTS. El método está basado en el uso de objetivos para construir taxonomías abstractas, bien fundamentadas y estables para lidiar con las características del mercado de COTS. Los nodos de las taxonomías son caracterizados por objetivos, sus relaciones son declaradas como dependencias y varios artefactos son construidos y gestionados para promover la reusabilidad y lidiar con la evolución constante.El método GOThIC ha sido elaborado a través de un proceso iterativo de investigación-acción para identificar los retos reales relacionados con el proceso de búsqueda de COTS. Posteriormente, las soluciones posibles fueron evaluadas e implementadas en varios casos de estudio en el ámbito industrial y académico en diversos dominios. Los resultados más relevantes fueron registrados y articulados en el método GOThIC. La evaluación industrial preliminar del método se ha llevado a cabo en algunas compañías en Noruega. / The process of building software systems by assembling and integrating pre-packaged solutions in the form of Commercial-Off-The-Shelf (COTS) software components has become a strategic need in a wide variety of application areas. In general, COTS components are software components that provide a specific functionality, available in the market to be purchased, interfaced and integrated into other software systems. The potential benefits of this technology are mainly its reduced costs and shorter development time, while maintaining the quality. Nevertheless, many challenges ranging form technical to legal issues must be faced for adapting the traditional software engineering activities in order to exploit these benefits.Nowadays there is an increasingly huge marketplace of COTS components; therefore, one of the most critical activities in COTS-based development is the selection of the components to be integrated into the system under development. Selection is basically composed of two main processes, namely: searching of candidates from the marketplace and their evaluation with respect to the system requirements. Unfortunately, most of the different existing methods for COTS selection focus their efforts on evaluation, letting aside the problem of searching components in the marketplace. Searching candidate COTS is not an easy task, having to cope with some challenging marketplace characteristics related to its widespread, evolvable and growing nature; and the lack of available and well-suited information to obtain a quality-assured search. Indeed, traditional reuse approaches also lack of appropriate solutions to reuse COTS components and the knowledge gained in each selection process. This lack of proposals is a serious drawback that makes the whole selection process highly risky, and often expensive and inefficient. This dissertation introduces the GOThIC (Goal- Oriented Taxonomy and reuse Infrastructure Construction) method aimed at building a domain reuse infrastructure for facilitating COTS components searching and reuse. It is based on goal-oriented approaches for building abstract, well-founded and stable taxonomies capable of dealing with the COTS marketplace characteristics. Thus, the nodes of these taxonomies are characterized by means of goals, their relationships declared as dependencies among them and several artifacts are constructed and managed for reusability and evolution purposes. The GOThIC method has been elaborated following an iterative process based on action research premises to identify the actual challenges related to COTS components searching. Then, possible solutions were envisaged and implemented by several industrial and academic case studies in different domains. Successful results were recorded to articulate the synergic GOThIC method solution, followed by its preliminary industrial evaluation in some Norwegian companies.