Sistema de apoio à decisão escolar

Especialmente em tempos de crise, como os que se têm vindo a viver atualmente, sobressai ainda mais a necessidade das nossas organizações serem bem geridas, e para que tal aconteça, torna-se importante medir indicadores que ajudem a acompanhar e compreender a evolução, nas diversas áreas. Para além desta conjuntura, nas últimas décadas o setor da Educação em Portugal tem-se deparado com enormes problemas e desa os, como por exemplo o nanciamento ou mais recentemente a (in)disciplina. Com este trabalho pretende-se contribuir com uma solução tecnológica, que visa em último caso a qualidade geral do ensino. Assim, na área da Educação torna-se importante, senão imperioso, dotar os decisores, com Sistemas de Apoio à Decisão que meçam indicadores de desempenho, e aumentem o grau de qualidade da informação disponibilizada a esses decisores, para que a gestão seja o mais pro ssional e rigorosa possível nas diversas vertentes, quer sejam no domínio escolar, nanceiro ou outro. A empresa Codevision tem vindo a implementar em diversas instituições de ensino e formação, o sistema de gestão escolar E-Schooling Server, um sistema de informação que dá suporte aos diversos processos de negócio dessas instituições. Com o objetivo de elevar a fasquia da qualidade na entrega de informação aos decisores da área escolar, decidiu apoiar o desenvolvimento de um sistema que complemente o E-Schooling Server. Nesse âmbito este trabalho aborda o desenvolvimento de um Sistema de Apoio à Decisão Escolar e na primeira fase subdivide-se: no desenvolvimento de um projeto de ETL automatizado, na criação de um DW, e na análise dos dados com ferramentas de BI. Na segunda fase é desenvolvida uma aplicação web, que permite ao utilizador nal a consulta de informação previamente tratada, através de grá cos. Um aspeto importante é o facto de a informação, tratada na primeira fase do trabalho, poder ser acedida e analisada através de qualquer aplicação com acesso a sistemas OLAP, não cando desta forma dependente da aplicação web. Por m, são apresentados e discutidos resultados, através de uma simulação com dados carregados no sistema desenvolvido, e que permite demonstrar a mais-valia que sistemas deste género representam, para os decisores de instituições de ensino e formação.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

An ecosystem of products and systems for ambient intelligence – the AAL4ALL users perspective

Developed societies are currently facing severe demographic changes: the world is getting older at an unprecedented rate. In 2000, about 420 million people, or approximately 7 percent of the world population, were aged 65 or older. By 2050, that number will be nearly 1.5 billion people, about 16 percent of the world population. This demographic trend will be also followed by an increase of people with physical limitations. New challenges will be raised to the traditional health care systems, not only in Portugal, but also in all other European states. There is an urgent need to find solutions that allow extending the time people can live in their preferred environment by increasing their autonomy, self-confidence and mobility. AAL4ALL presents an idea for an answer through the development of an ecosystem of products and services for Ambient Assisted Living (AAL) associated to a business model and validated through large scale trial. This paper presents the results of the first survey developed within the AAL4ALL project: the users’ survey targeted at the Portuguese seniors and pre-seniors. This paper is, thus, about the lives of the Portuguese population aged 50 and over.

On the discovery of business processes orchestration patterns

COORDINSPECTOR is a Software Tool aiming at extracting the coordination layer of a software system. Such a reverse engineering process provides a clear view of the actually invoked services as well as the logic behind such invocations. The analysis process is based on program slicing techniques and the generation of, System Dependence Graphs and Coordination Dependence Graphs. The tool analyzes Common Intermediate Language (CIL), the native language of the Microsoft .Net Framework, thus making suitable for processing systems developed in any .Net Framework compilable language. COORDINSPECTOR generates graphical representations of the coordination layer together with business process orchestrations specified in WSBPEL 2.0