Evolutionary computing has become an alternative to classical computational models since it can solve, efficiently, hard scientific problems in polynomial time. Workflows support the automation of scientific processes, providing mechanisms that underpin modern computational science. However, the traditional workflows offer little support to evolutionary computing. Thus, to bridge this gap, this paper describes the VisPyGMO. It extends the existing scientific workflow management systems by offering a range of reusable evolutionary algorithms modules. Besides, we present a use case using evolutionary algorithms in VisTrails system to analyze more than 20 years of historical data retrieved from Kaggle platform and to predict pesticide consumption in fresh foods. The results presented demonstrate the feasibility of the proposal and demonstrate that significant optimization can be achieved with an evolutionary approach for the optimization of state-of-the-art scientific workflows.

This paper demonstrates Treo, a natural language query mechanism for Linked Data graphs. The approach uses a distributional semantic vector space model to semantically match user query terms with data, supporting vocabulary-independent (or schema-agnostic) queries over structured data.

Social networks are sets of links that organize people, groups, and institutions in an equal and democratic way, and around a common purpose [4]. During the formation of such networks, problems can arise, such as elements that concentrate many relationships, very isolated individuals or peripheral members of a network, people who are the only link between two distinct groups, agglomerations of people in isolated points. This can cause a series of communications problems, which ultimately will cause losses to the most important element that flows through social networks: knowledge. Thus it is necessary a series of analysis on social networks so we can monitor possible collaborations and assist in the formation of work teams, this can be done through the concept of balancing social networks developed by us [20], whose main focus is to maintain the social network as cohesive as possible in order to maximize the flow of knowledge. A practical application of these analysis occurred in the project BRINCA (Analysis and Balancing of Scientific Social Networks in Cancer Control). BRINCA's goals are to analyze how members of the Cancer INCT (National Institute of Science and Technology) collaborate and how the scientific knowledge flows amongst the different researchers and institutions. With this project we could perform contextualized analysis (i.e. by relationship type, subject, institution, geographic location, time) and use the metrics of social network analysis to understand and verify how collaboration occurs in a multi-disciplinary, multi-team project. Moreover, we used the criteria of social network analysis to perform the balancing. In addition, some of the analysis made on the scenery of cancer are shown.