Nuevos Esquemas de Visualizaciones para Mejorar la Comprensibilidad de Modelos de Data Mining

La gran cantidad de datos que se registran diariamente en los sistemas de base de datos de las organizaciones ha generado la necesidad de analizarla. Sin embargo, se enfrentan a la complejidad de procesar enormes volúmenes de datos a través de métodos tradicionales de análisis. Además, dentro de un contexto globalizado y competitivo las organizaciones se mantienen en la búsqueda constante de mejorar sus procesos, para lo cual requieren herramientas que les permitan tomar mejores decisiones. Esto implica estar mejor informado y conocer su historia digital para describir sus procesos y poder anticipar (predecir) eventos no previstos. Estos nuevos requerimientos de análisis de datos ha motivado el desarrollo creciente de proyectos de minería de datos. El proceso de minería de datos busca obtener desde un conjunto masivo de datos, modelos que permitan describir los datos o predecir nuevas instancias en el conjunto. Implica etapas de: preparación de los datos, procesamiento parcial o totalmente automatizado para identificar modelos en los datos, para luego obtener como salida patrones, relaciones o reglas. Esta salida debe significar un nuevo conocimiento para la organización, útil y comprensible para los usuarios finales, y que pueda ser integrado a los procesos para apoyar la toma de decisiones. Sin embargo, la mayor dificultad es justamente lograr que el analista de datos, que interviene en todo este proceso, pueda identificar modelos lo cual es una tarea compleja y muchas veces requiere de la experiencia, no sólo del analista de datos, sino que también del experto en el dominio del problema. Una forma de apoyar el análisis de datos, modelos y patrones es a través de su representación visual, utilizando las capacidades de percepción visual del ser humano, la cual puede detectar patrones con mayor facilidad. Bajo este enfoque, la visualización ha sido utilizada en minería datos, mayormente en el análisis descriptivo de los datos (entrada) y en la presentación de los patrones (salida), dejando limitado este paradigma para el análisis de modelos. El presente documento describe el desarrollo de la Tesis Doctoral denominada “Nuevos Esquemas de Visualizaciones para Mejorar la Comprensibilidad de Modelos de Data Mining”. Esta investigación busca aportar con un enfoque de visualización para apoyar la comprensión de modelos minería de datos, para esto propone la metáfora de modelos visualmente aumentados. ABSTRACT The large amount of data to be recorded daily in the systems database of organizations has generated the need to analyze it. However, faced with the complexity of processing huge volumes of data over traditional methods of analysis. Moreover, in a globalized and competitive environment organizations are kept constantly looking to improve their processes, which require tools that allow them to make better decisions. This involves being bettered informed and knows your digital story to describe its processes and to anticipate (predict) unanticipated events. These new requirements of data analysis, has led to the increasing development of data-mining projects. The data-mining process seeks to obtain from a massive data set, models to describe the data or predict new instances in the set. It involves steps of data preparation, partially or fully automated processing to identify patterns in the data, and then get output patterns, relationships or rules. This output must mean new knowledge for the organization, useful and understandable for end users, and can be integrated into the process to support decision-making. However, the biggest challenge is just getting the data analyst involved in this process, which can identify models is complex and often requires experience not only of the data analyst, but also the expert in the problem domain. One way to support the analysis of the data, models and patterns, is through its visual representation, i.e., using the capabilities of human visual perception, which can detect patterns easily in any context. Under this approach, the visualization has been used in data mining, mostly in exploratory data analysis (input) and the presentation of the patterns (output), leaving limited this paradigm for analyzing models. This document describes the development of the doctoral thesis entitled "New Visualizations Schemes to Improve Understandability of Data-Mining Models". This research aims to provide a visualization approach to support understanding of data mining models for this proposed metaphor visually enhanced models.

Applying ontologies to business and systems modelling techniques and perspectives: Lessons learned

For many years in the area of business systems analysis and design, practitioners and researchers alike have been searching for some comprehensive basis on which to evaluate, compare, and engineer techniques that are promoted for use in the modelling of systems' requirements. To date, while many frameworks, factors, and facets have been forthcoming, none appear to be based on a sound theory. In light of this dilemma, over the last 10 years, attention has been devoted by researchers to the use of ontology to provide some theoretical basis for the advancement of the business systems modelling discipline. This paper outlines how we have used a particular ontology for this purpose over the last five years. In particular we have learned that the understandability and the applicability of the selected ontology must be clear for IS professionals, the results of any ontological evaluation must be tempered by economic efficiency considerations of the stakeholders involved, and ontologies may have to be focused for the business purpose and type of user involved in the modelling situation.

Using integrated metamodeling to define OO design patterns with object-z and UML

Three important goals in describing software design patterns are: generality, precision, and understandability. To address these goals, this paper presents an integrated approach to specifying patterns using Object-Z and UML. To achieve the generality goal, we adopt a role-based metamodeling approach to define patterns. With this approach, each pattern is defined as a pattern role model. To achieve precision, we formalize role concepts using Object-Z (a role metamodel) and use these concepts to define patterns (pattern role models). To achieve understandability, we represent the role metamodel and pattern role models visually using UML. Our pattern role models provide a precise basis for pattern-based model transformations or refactoring approaches.

Accounting for derivatives:an evaluation of reporting practice by UK banks

In 1998 the Accounting Standards Board (ASB) published FRS 13, ‘Derivatives and other Financial Instruments: Disclosures’. This laid down the requirements for disclosures of an entity’s policies, objectives and strategies in using financial instruments, their impact on its risk, performance and financial condition, and details of how risks are managed. FRS 13 became effective in March 1999, and this paper uses the 1999 annual reports of UK banks to evaluate the usefulness of disclosures from a user’s perspective. Usefulness is measured in terms of the criteria of materiality, relevance, reliability, comparability and understandability as defined in the ASB’s Statement of Principles (ASB, 1999). Our findings suggest that the narrative disclosures are generic in nature, the numerical data incomplete and not always comparable, and that it is difficult for the user to combine both narrative and numerical information in order to assess the banks’ risk profile. Our overall conclusion is therefore that current UK financial reporting practices are of limited help to users wishing to assess the scale of an institution’s financial risk exposure.

Extending Automated Testing To High-level Software Requirements : A study on the feasibility of automated acceptance-testing

Automated acceptance testing is the testing of software done in higher level to test whether the system abides by the requirements desired by the business clients by the use of piece of script other than the software itself. This project is a study of the feasibility of acceptance tests written in Behavior Driven Development principle. The project includes an implementation part where automated accep- tance testing is written for Touch-point web application developed by Dewire (a software consultant company) for Telia (a telecom company) from the require- ments received from the customer (Telia). The automated acceptance testing is in Cucumber-Selenium framework which enforces Behavior Driven Development principles. The purpose of the implementation is to verify the practicability of this style of acceptance testing. From the completion of implementation, it was concluded that all the requirements from customer in real world can be converted into executable specifications and the process was not at all time-consuming or difficult for a low-experienced programmer like the author itself. The project also includes survey to measure the learnability and understandability of Gherkin- the language that Cucumber understands. The survey consist of some Gherkin exam- ples followed with questions that include making changes to the Gherkin exam- ples. Survey had 3 parts: first being easy, second medium and third most difficult. Survey also had a linear scale from 1 to 5 to rate the difficulty level for each part of the survey. 1 stood for very easy and 5 for very difficult. Time when the partic- ipants began the survey was also taken in order to calculate the total time taken by the participants to learn and answer the questions. Survey was taken by 18 of the employers of Dewire who had primary working role as one of the programmer, tester and project manager. In the result, tester and project manager were grouped as non-programmer. The survey concluded that it is very easy and quick to learn Gherkin. While the participants rated Gherkin as very easy.

Determinantes da legibilidade e compreensibilidade dos relatórios de gestão de risco: evidência nas empresas cotadas portuguesas

O presente trabalho pretende analisar a divulgação do risco nos relatórios anuais das empresas portuguesas não financeiras, com valores cotados em bolsa. No momento em que vivemos, com toda esta instabilidade, os investidores e outros stakeholders estão cada vez menos confiantes e mais exigentes. Assim, relatar informação sobre risco, começa a ser um dos meios utilizados pelas empresas para transmitir confiança e viabilidade ao exterior. Contudo, será que uma empresa que divulga sobre risco é uma empresa que se encontra totalmente sã, e que não oculta nem ofusca qualquer tipo de informação? O objetivo deste trabalho passará por apurar, se de alguma forma, os gestores se fazem valer das estratégias de impression management para ocultar ou, ofuscar os stakeholders na divulgação de informações sobre risco. Para o desenvolvimento desta investigação tivemos por base as empresas cotadas na Euronext Lisbon, para as quais foi efetuada uma análise de conteúdo do Relatório de Gestão, do Anexo e do Relatório do Governo das Sociedades, nos anos de 2007, 2010 e 2013. Aos dados recolhidos aplicou-se o modelo de regressão OLS, confirmando a hipótese do índice de compreensibilidade estar associado positivamente com a dimensão da empresa. Dos resultados obtidos concluiu-se ainda a existência de uma associação negativa entre o índice de legibilidade e a dimensão e o setor de atividade.