Public Participation geographic information systems in developing countries: lessons from Uganda on the impacts of interactive screens PPGIS and community perceptions of space

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Web Processing Services for Forestry and Environmental Applications

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Creation of a web application for smart park system and parking prediction study for system integration

In the last few years, we have observed an exponential increasing of the information systems, and parking information is one more example of them. The needs of obtaining reliable and updated information of parking slots availability are very important in the goal of traffic reduction. Also parking slot prediction is a new topic that has already started to be applied. San Francisco in America and Santander in Spain are examples of such projects carried out to obtain this kind of information. The aim of this thesis is the study and evaluation of methodologies for parking slot prediction and the integration in a web application, where all kind of users will be able to know the current parking status and also future status according to parking model predictions. The source of the data is ancillary in this work but it needs to be understood anyway to understand the parking behaviour. Actually, there are many modelling techniques used for this purpose such as time series analysis, decision trees, neural networks and clustering. In this work, the author explains the best techniques at this work, analyzes the result and points out the advantages and disadvantages of each one. The model will learn the periodic and seasonal patterns of the parking status behaviour, and with this knowledge it can predict future status values given a date. The data used comes from the Smart Park Ontinyent and it is about parking occupancy status together with timestamps and it is stored in a database. After data acquisition, data analysis and pre-processing was needed for model implementations. The first test done was with the boosting ensemble classifier, employed over a set of decision trees, created with C5.0 algorithm from a set of training samples, to assign a prediction value to each object. In addition to the predictions, this work has got measurements error that indicates the reliability of the outcome predictions being correct. The second test was done using the function fitting seasonal exponential smoothing tbats model. Finally as the last test, it has been tried a model that is actually a combination of the previous two models, just to see the result of this combination. The results were quite good for all of them, having error averages of 6.2, 6.6 and 5.4 in vacancies predictions for the three models respectively. This means from a parking of 47 places a 10% average error in parking slot predictions. This result could be even better with longer data available. In order to make this kind of information visible and reachable from everyone having a device with internet connection, a web application was made for this purpose. Beside the data displaying, this application also offers different functions to improve the task of searching for parking. The new functions, apart from parking prediction, were: - Park distances from user location. It provides all the distances to user current location to the different parks in the city. - Geocoding. The service for matching a literal description or an address to a concrete location. - Geolocation. The service for positioning the user. - Parking list panel. This is not a service neither a function, is just a better visualization and better handling of the information.

Adaption of the Creative Commons approach and the roaming concept to spatial data infrastructures (SDI)

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

Creation and extension of ontologies for describing communications in the context of organizations

Thesis submitted to Faculdade de Ciências e Tecnologia of the Universidade Nova de Lisboa, in partial fulfillment of the requirements for the degree of Master in Computer Science

ZoomAzores project: implementation of a WebGIS for Nature and Adventure Tourism.

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

An information model for lean, agile, resilient and green supply chain management

Dissertação para a obtenção de Grau de Mestre em Engenharia e Gestão Industrial

A framework for supporting knowledge representation – an ontological based approach

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

Métodos de avaliação da frequência de eventos adversos em contexto hospitalar : contributos de uma revisão sistemática

RESUMO - Os eventos adversos (EA) hospitalares constituem um problema sério dos cuidados de saúde com consequências clínicas, económicas e sociais para a Saúde Pública. Nas últimas décadas foram realizados diversos estudos com o objetivo de conhecer de forma mais pormenorizada esta realidade, nomeadamente no que diz respeito à frequência, tipologia, evitabilidade e impacte dos EA. De entre as diferentes metodologias que têm sido utilizadas parece existir algum consenso em torno da análise retrospetiva de processos clínicos como a que oferece maior garantia de fiabilidade e reprodutibilidade, não obstante as limitações conhecidas. Assim, propusemo-nos com este trabalho, analisar as vantagens e desvantagens dos métodos mais comummente utilizados para caraterizar a ocorrência de EA e, concomitantemente elaborar uma revisão sistemática (RS) dos estudos que aplicaram o método de revisão retrospetiva de processos clínicos na caraterização e avaliação dos EA em contexto hospitalar. Para definir a nossa amostra, realizámos uma pesquisa formal nas bases de dados MEDLINE e Web of Knowledge, e foi realizado um cruzamento manual de referências dos artigos elegíveis para identificar estudos adicionais relevantes. Os artigos selecionados foram revistos independentemente no que diz respeito à metodologia, aos critérios de elegibilidade e aos objetivos. Durante a fase de revisão e aplicação dos critérios de inclusão e exclusão foram selecionados os artigos que abordassem a frequência/incidência e a percentagem de evitabilidade dos EA hospitalares, através da aplicação do método de revisão retrospetiva de processos clínicos. Após a fase de pesquisa e revisão dos artigos, foram selecionados para a nossa amostra oito estudos que incluíram um total de 28.862 processos clínicos revistos. De entre os principais resultados encontrados destaca-se: i) A mediana de incidência de EA hospitalares de 9.5%; Universidade Nova de Lisboa – Escola Nacional de Saúde Pública ii) O valor de mediana de EA considerados evitáveis de 45.5%; iii) No que se refere ao impacte clínico dos EA, mais de metade dos doentes (56.3%) não experienciou incapacidade ou experienciou incapacidade menor; iv) Em 8% dos casos de EA ocorreu a morte dos doentes. v) Quanto ao impacte económico evidencia-se o facto de, nos doentes em que se confirmou EA, o período de internamento se ter prolongado, em média, por 7.1 dias com consequentes e previsíveis custos adicionais. Tendo em consideração as vantagens e desvantagens de cada método, os sistemas de informação existentes em Portugal e a realidade das instituições de saúde, parece-nos plausível destacar o método de revisão dos processos clínicos como o que melhor se adapta para caraterizar os EA no contexto hospitalar português.

Narrative Bifurcation, Cloud Computing Interfaces and Hitchcok

From a narratological perspective, this paper aims to address the theoretical issues concerning the functioning of the so called «narrative bifurcation» in data presentation and information retrieval. Its use in cyberspace calls for a reassessment as a storytelling device. Films have shown its fundamental role for the creation of suspense. Interactive fiction and games have unveiled the possibility of plots with multiple choices, giving continuity to cinema split-screen experiences. Using practical examples, this paper will show how this storytelling tool returns to its primitive form and ends up by conditioning cloud computing interface design.

Reconceptualising public spaces of (IN)equality: sensing and creating layers of visibility

Tese apresentada para cumprimento dos requisitos necessários à obtenção do grau de Doutor em Geografia e Planeamento Territorial - Especialidade: Geografia Humana

Automatic extraction of concepts from texts and applications

The extraction of relevant terms from texts is an extensively researched task in Text- Mining. Relevant terms have been applied in areas such as Information Retrieval or document clustering and classification. However, relevance has a rather fuzzy nature since the classification of some terms as relevant or not relevant is not consensual. For instance, while words such as "president" and "republic" are generally considered relevant by human evaluators, and words like "the" and "or" are not, terms such as "read" and "finish" gather no consensus about their semantic and informativeness. Concepts, on the other hand, have a less fuzzy nature. Therefore, instead of deciding on the relevance of a term during the extraction phase, as most extractors do, I propose to first extract, from texts, what I have called generic concepts (all concepts) and postpone the decision about relevance for downstream applications, accordingly to their needs. For instance, a keyword extractor may assume that the most relevant keywords are the most frequent concepts on the documents. Moreover, most statistical extractors are incapable of extracting single-word and multi-word expressions using the same methodology. These factors led to the development of the ConceptExtractor, a statistical and language-independent methodology which is explained in Part I of this thesis. In Part II, I will show that the automatic extraction of concepts has great applicability. For instance, for the extraction of keywords from documents, using the Tf-Idf metric only on concepts yields better results than using Tf-Idf without concepts, specially for multi-words. In addition, since concepts can be semantically related to other concepts, this allows us to build implicit document descriptors. These applications led to published work. Finally, I will present some work that, although not published yet, is briefly discussed in this document.

Learning domain-specific sentiment lexicons with applications to recommender systems

Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation.