Model-based systems engineering: a system for traffic & environment

The contemporary world is crowded of large, interdisciplinary, complex systems made of other systems, personnel, hardware, software, information, processes, and facilities. The Systems Engineering (SE) field proposes an integrated holistic approach to tackle these socio-technical systems that is crucial to take proper account of their multifaceted nature and numerous interrelationships, providing the means to enable their successful realization. Model-Based Systems Engineering (MBSE) is an emerging paradigm in the SE field and can be described as the formalized application of modelling principles, methods, languages, and tools to the entire lifecycle of those systems, enhancing communications and knowledge capture, shared understanding, improved design precision and integrity, better development traceability, and reduced development risks. This thesis is devoted to the application of the novel MBSE paradigm to the Urban Traffic & Environment domain. The proposed system, the GUILTE (Guiding Urban Intelligent Traffic & Environment), deals with a present-day real challenging problem “at the agenda” of world leaders, national governors, local authorities, research agencies, academia, and general public. The main purposes of the system are to provide an integrated development framework for the municipalities, and to support the (short-time and real-time) operations of the urban traffic through Intelligent Transportation Systems, highlighting two fundamental aspects: the evaluation of the related environmental impacts (in particular, the air pollution and the noise), and the dissemination of information to the citizens, endorsing their involvement and participation. These objectives are related with the high-level complex challenge of developing sustainable urban transportation networks. The development process of the GUILTE system is supported by a new methodology, the LITHE (Agile Systems Modelling Engineering), which aims to lightening the complexity and burdensome of the existing methodologies by emphasizing agile principles such as continuous communication, feedback, stakeholders involvement, short iterations and rapid response. These principles are accomplished through a universal and intuitive SE process, the SIMILAR process model (which was redefined at the light of the modern international standards), a lean MBSE method, and a coherent System Model developed through the benchmark graphical modeling languages SysML and OPDs/OPL. The main contributions of the work are, in their essence, models and can be settled as: a revised process model for the SE field, an agile methodology for MBSE development environments, a graphical tool to support the proposed methodology, and a System Model for the GUILTE system. The comprehensive literature reviews provided for the main scientific field of this research (SE/MBSE) and for the application domain (Traffic & Environment) can also be seen as a relevant contribution.

Mining biomedical information from scientific literature

The rapid evolution and proliferation of a world-wide computerized network, the Internet, resulted in an overwhelming and constantly growing amount of publicly available data and information, a fact that was also verified in biomedicine. However, the lack of structure of textual data inhibits its direct processing by computational solutions. Information extraction is the task of text mining that intends to automatically collect information from unstructured text data sources. The goal of the work described in this thesis was to build innovative solutions for biomedical information extraction from scientific literature, through the development of simple software artifacts for developers and biocurators, delivering more accurate, usable and faster results. We started by tackling named entity recognition - a crucial initial task - with the development of Gimli, a machine-learning-based solution that follows an incremental approach to optimize extracted linguistic characteristics for each concept type. Afterwards, Totum was built to harmonize concept names provided by heterogeneous systems, delivering a robust solution with improved performance results. Such approach takes advantage of heterogenous corpora to deliver cross-corpus harmonization that is not constrained to specific characteristics. Since previous solutions do not provide links to knowledge bases, Neji was built to streamline the development of complex and custom solutions for biomedical concept name recognition and normalization. This was achieved through a modular and flexible framework focused on speed and performance, integrating a large amount of processing modules optimized for the biomedical domain. To offer on-demand heterogenous biomedical concept identification, we developed BeCAS, a web application, service and widget. We also tackled relation mining by developing TrigNER, a machine-learning-based solution for biomedical event trigger recognition, which applies an automatic algorithm to obtain the best linguistic features and model parameters for each event type. Finally, in order to assist biocurators, Egas was developed to support rapid, interactive and real-time collaborative curation of biomedical documents, through manual and automatic in-line annotation of concepts and relations. Overall, the research work presented in this thesis contributed to a more accurate update of current biomedical knowledge bases, towards improved hypothesis generation and knowledge discovery.