Enterprise reference lexicon building from business models

Nowadays, a significant increase on the demand for interoperable systems for exchanging data in business collaborative environments has been noticed. Consequently, cooperation agreements between each of the involved enterprises have been brought to light. However, due to the fact that even in a same community or domain, there is a big variety of knowledge representation not semantically coincident, which embodies the existence of interoperability problems in the enterprises information systems that need to be addressed. Moreover, in relation to this, most organizations face other problems about their information systems, as: 1) domain knowledge not being easily accessible by all the stakeholders (even intra-enterprise); 2) domain knowledge not being represented in a standard format; 3) and even if it is available in a standard format, it is not supported by semantic annotations or described using a common and understandable lexicon. This dissertation proposes an approach for the establishment of an enterprise reference lexicon from business models. It addresses the automation in the information models mapping for the reference lexicon construction. It aggregates a formal and conceptual representation of the business domain, with a clear definition of the used lexicon to facilitate an overall understanding by all the involved stakeholders, including non-IT personnel.

Semantic relations extraction from unstructured information for domain ontologies enrichment

Based in internet growth, through semantic web, together with communication speed improvement and fast development of storage device sizes, data and information volume rises considerably every day. Because of this, in the last few years there has been a growing interest in structures for formal representation with suitable characteristics, such as the possibility to organize data and information, as well as the reuse of its contents aimed for the generation of new knowledge. Controlled Vocabulary, specifically Ontologies, present themselves in the lead as one of such structures of representation with high potential. Not only allow for data representation, as well as the reuse of such data for knowledge extraction, coupled with its subsequent storage through not so complex formalisms. However, for the purpose of assuring that ontology knowledge is always up to date, they need maintenance. Ontology Learning is an area which studies the details of update and maintenance of ontologies. It is worth noting that relevant literature already presents first results on automatic maintenance of ontologies, but still in a very early stage. Human-based processes are still the current way to update and maintain an ontology, which turns this into a cumbersome task. The generation of new knowledge aimed for ontology growth can be done based in Data Mining techniques, which is an area that studies techniques for data processing, pattern discovery and knowledge extraction in IT systems. This work aims at proposing a novel semi-automatic method for knowledge extraction from unstructured data sources, using Data Mining techniques, namely through pattern discovery, focused in improving the precision of concept and its semantic relations present in an ontology. In order to verify the applicability of the proposed method, a proof of concept was developed, presenting its results, which were applied in building and construction sector.

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.

Discovery and retrieval of Geographic data using Google

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

How human schematization and systematic errors take effect on sketch map formalizations

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

Framework for a business interoperability quotient measurement model

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova da Lisboa para obtenção do grau de Mestre em Engenharia e Gestão Industrial (MEGI)

Exploratory geospatial data analysis using self-organizing maps

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science.

A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Systems.

SOLAP+: extending the interaction model

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

Municipal solid waste management system: decision support through systems analysis

Thesis submitted to the Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia for the degree of Doctor of Philosophy in Environmental Engineering

Regional change in the Algarve: A Geographic Information System approach

Thesis submitted to the Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Information Management – Geographic Information Systems

Parallel programming in biomedical signal processing

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Evaluation of a volunteered geographical information trust measure in the case of OpenStreetMap

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

Providing energy efficiency location-based strategies for buildings using linked open data

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

GI Systems for public health with an ontology based approach

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