Abstract
Scientific research has always been done and is done in a collaborative manner. Today we have available the internet that facilitates this process through communication tools, data sharing, management tasks, among others. However, qualitative research has taken timid steps towards a truly collaborative work. This paper presents the 4C collaborative work model as well as the collaboration features available in the current version (2.0) of the qualitative analysis software webQDA. The paper is based on a questionnaire sought to understand the views of a random sample of users in Brazil, Spain and Portugal about these features. The results achieved demonstrate that the communications capabilities, cooperation and coordination are not yet fully explored by researchers. We hope that the development of version 3.0 of webQDA, can be benefited by the identification of the features most exploited by researchers and point to desired new features to be made available.

Abstract
This paper proposes an ontology-based approach to visualizing urban mobility data. Our approach, which is in ongoing development, is centered in a visualization-oriented urban mobility ontology that is used to semantically characterize data and visualization techniques. We present a practical application to a public transportation network of the city of Porto, Portugal. We address how semantics can empower and facilitate tasks like automatic recommendation of visualization techniques, and definition of a data filter based on passengers' journey patterns.

Abstract
Portable digital devices equipped with GPS antennas are ubiquitous sources of continuous information for location-based Expert and Intelligent Systems. The availability of these traces on the human mobility patterns is growing explosively. To mine this data is a fascinating challenge which can produce a big impact on both travelers and transit agencies. This paper proposes a novel incremental framework to maintain statistics on the urban mobility dynamics over a time-evolving origin-destination (O-D) matrix. The main motivation behind such task is to be able to learn from the location-based samples which are continuously being produced, independently on their source, dimensionality or (high) communicational rate. By doing so, the authors aimed to obtain a generalist framework capable of summarizing relevant context-aware information which is able to follow, as close as possible, the stochastic dynamics on the human mobility behavior. Its potential impact ranges Expert Systems for decision support across multiple industries, from demand estimation for public transportation planning till travel time prediction for intelligent routing systems, among others. The proposed methodology settles on three steps: (i) Half-Space trees are used to divide the city area into dense subregions of equal mass. The uncovered regions form an O-D matrix which can be updated by transforming the trees'leaves into conditional nodes (and vice-versa). The (ii) Partioning Incremental Algorithm is then employed to discretize the target variable's historical values on each matrix cell. Finally, a (iii) dimensional hierarchy is defined to discretize the domains of the independent variables depending on the cell's samples. A Taxi Network running on a mid-sized city in Portugal was selected as a case study. The Travel Time Estimation (TTE) problem was regarded as a real-world application. Experiments using one million data samples were conducted to validate the methodology. The results obtained highlight the straightforward contribution of this method: it is capable of resisting to the drift while still approximating context-aware solutions through a multidimensional discretization of the feature space. It is a step ahead in estimating the real-time mobility dynamics, regardless of its application field.

Abstract
We address optimal control problems for nonlinear systems with pathwise state-constraints. These are challenging nonlinear problems for which the number of discretization points is a major factor determining the computational time. Also, the location of these points has a major impact in the accuracy of the solutions. We propose an algorithm that iteratively finds an adequate time-grid to satisfy some predefined error estimate on the obtained trajectories, which is guided by information on the adjoint multipliers. The obtained results show a highly favorable comparison against the traditional equidistant spaced time grid methods, including the ones using discrete time models. This way, continuous time plant models can be directly used. The discretization procedure can be automated and there is no need to select a priori the adequate time step. Even if the optimization procedure is forced to stop in an early stage, as might be the case in real time problems, we can still obtain a meaningful solution, although it might be a less accurate one. The extension of the procedure to a Model Predictive Control (MPC) context is proposed here. By defining a time dependent accuracy threshold, we can generate solutions that are more accurate in the initial parts of the receding horizon, which are the most relevant for MPC.

Abstract
Seville, with a metropolitan population of about 1.5 million, is the capital and largest city of Andalusia (S Spain). It is the 30th most populous municipality in the European Union and contains three UNESCO World Heritage Sites. The Seville harbour, located about 80 km from the Atlantic Ocean, is the only river port in Spain. The city is located on the plain of the Guadalquivir River. Using Multi-Temporal InSAR with ERS-1/2 and Envisat data a subsidence behavior is detected in the period 1992-2010. The geometry of the subsiding areas suggests that it should be conditioned by the fluvial dynamics of the Guadalquivir River and its tributaries. Facies distribution along the fluvial system (paleochannels, flood plains.), with different grain size and matrix proportion, may explain the relative subsidence between the different sectors.

Abstract
We present a dynamic web tool that allows interactive search and visualization of large news archives using an entity-centric approach. Users are able to search entities using keyword phrases expressing news stories or events and the system retrieves the most relevant entities to the user query based on automatically extracted and indexed entity profiles. From the computational journalism perspective, TimeMachine allows users to explore media content through time using automatic identification of entity names, jobs, quotations and relations between entities from co-occurrences networks extracted from the news articles. TimeMachine demo is available at http://maquinadotempo.sapo.pt/.