Semantic adaptability for the systems interoperability

In the current global and competitive business context, it is essential that enterprises adapt their knowledge resources in order to smoothly interact and collaborate with others. However, due to the existent multiculturalism of people and enterprises, there are different representation views of business processes or products, even inside a same domain. Consequently, one of the main problems found in the interoperability between enterprise systems and applications is related to semantics. The integration and sharing of enterprises knowledge to build a common lexicon, plays an important role to the semantic adaptability of the information systems. The author proposes a framework to support the development of systems to manage dynamic semantic adaptability resolution. It allows different organisations to participate in a common knowledge base building, letting at the same time maintain their own views of the domain, without compromising the integration between them. Thus, systems are able to be aware of new knowledge, and have the capacity to learn from it and to manage its semantic interoperability in a dynamic and adaptable way. The author endorses the vision that in the near future, the semantic adaptability skills of the enterprise systems will be the booster to enterprises collaboration and the appearance of new business opportunities.

How to improve urban mobility in Lisbon: intermodality and information and ticketing systems

Urban mobility is one of the main challenges facing urban areas due to the growing population and to traffic congestion, resulting in environmental pressures. The pathway to urban sustainable mobility involves strengthening of intermodal mobility. The integrated use of different transport modes is getting more and more important and intermodality has been mentioned as a way for public transport compete with private cars. The aim of the current dissertation is to define a set of strategies to improve urban mobility in Lisbon and by consequence reduce the environmental impacts of transports. In order to do that several intermodal practices over Europe were analysed and the transport systems of Brussels and Lisbon were studied and compared, giving special attention to intermodal systems. In the case study was gathered data from both cities in the field, by using and observing the different transport modes, and two surveys were done to the cities users. As concluded by the study, Brussels and Lisbon present significant differences. In Brussels the measures to promote intermodality are evident, while in Lisbon a lot still needs to be done. It also made clear the necessity for improvements in Lisbon’s public transports to a more intermodal passenger transport system, through integration of different transport modes and better information and ticketing system. Some of the points requiring developments are: interchanges’ waiting areas; integration of bicycle in public transport; information about correspondences with other transport modes; real-time information to passengers pre-trip and on-trip, especially in buses and trams. After the identification of the best practices in Brussels and the weaknesses in Lisbon the possibility of applying some of the practices in Brussels to Lisbon was evaluated. Brussels demonstrated to be a good example of intermodality and for that reason some of the recommendations to improve intermodal mobility in Lisbon can follow the practices in place in Brussels.

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.

Design of a case-based reasoner for information security in military organizations

Information security is concerned with the protection of information, which can be stored, processed or transmitted within critical information systems of the organizations, against loss of confidentiality, integrity or availability. Protection measures to prevent these problems result through the implementation of controls at several dimensions: technical, administrative or physical. A vital objective for military organizations is to ensure superiority in contexts of information warfare and competitive intelligence. Therefore, the problem of information security in military organizations has been a topic of intensive work at both national and transnational levels, and extensive conceptual and standardization work is being produced. A current effort is therefore to develop automated decision support systems to assist military decision makers, at different levels in the command chain, to provide suitable control measures that can effectively deal with potential attacks and, at the same time, prevent, detect and contain vulnerabilities targeted at their information systems. The concept and processes of the Case-Based Reasoning (CBR) methodology outstandingly resembles classical military processes and doctrine, in particular the analysis of “lessons learned” and definition of “modes of action”. Therefore, the present paper addresses the modeling and design of a CBR system with two key objectives: to support an effective response in context of information security for military organizations; to allow for scenario planning and analysis for training and auditing processes.

Adoção e difusão de aplicações das tecnologias da informação: iniciativas lançadas no âmbito de processos de implantação de aplicações em organizações – estudo de práticas profissionais e proposta de ferramenta de suporte

Tese de Doutoramento Tecnologias e Sistemas de Informação

Desenvolvimento de sistema de alarmes para incontinentes

Dissertação de mestrado em redes e serviços telemáticos

Using a domain-specific language to enrich ETL schemas

Today it is easy to find a lot of tools to define data migration schemas among different types of information systems. Data migration processes use to be implemented on a very diverse range of applications, ranging from conventional operational systems to data warehousing platforms. The implementation of a data migration process often involves a serious planning, considering the development of conceptual migration schemas at early stages. Such schemas help architects and engineers to plan and discuss the most adequate way to migrate data between two different systems. In this paper we present and discuss a way for enriching data migration conceptual schemas in BPMN using a domain-specific language, demonstrating how to convert such enriched schemas to a first correspondent physical representation (a skeleton) in a conventional ETL implementation tool like Kettle.

Step towards of a homemade business intelligence solution: A case study in textile industry

Nowadays, organizations are increasingly looking to invest in business intelligence solutions, mainly private companies in order to get advantage over its competitors, however they do not know what is necessary. Business intelligence allows an analysis of consolidated information in order to obtain more specific outlets and certain indications in order to support the decision making process. You can take the right decision based on the data collected from different information systems present in the organization and outside of them. The textile sector is a sector where concept of Business Intelligence it is not many explored yet. Actually there are few textile companies that have a BI platform. Thus, the article objective is present an architecture and show all the steps by which companies need to spend to implement a successful free homemade Business Intelligence system. As result the proposed approach it was validated using real data aiming assess the steps defined.

Abstract computation in schizophrenia detection through artificial neural network based systems

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.

Caso de demonstração de uma framework para automatização do teste de APIs de aplicações SaaS

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Desenvolvimento e actualização do controlo de um equipamento de prototipagem rápida

Dissertação de mestrado integrado em Engenharia Mecânica

Avaliação do desempenho de equipas em serviços de sistemas de informação

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

As disfunções da memória organizacional e suas implicações no uso dos sistemas de informação: um caso de estudo

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Modelação de processos de negócio: análise comparativa de linguagens

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Apoio à decisão na análise inteligente de reclamações

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação