Massimiliano Zanin

Pau Esteve, Massimiliano Zanin

The generation of synthetic data is receiving increasing attention from the scientific community, thanks to its ability to solve problems like data scarcity and privacy, and is starting to find applications in air transport. We here tackle the problem of generating synthetic, yet realistic, time series of delays at airports, starting from large collections of operations in Europe and the US. We specifically compare three models, two of them based on state of the art Deep Learning algorithms, and one simplified Genetic Algorithm approach. We show how the latter can generate time series that are almost indistinguishable from real ones, while maintaining a high variability. We further validate the resulting time series in a problem of detecting delay propagations between airports. We finally make the synthetic data available to the scientific community.

Eduardo P. Garcia del Valle, Gerardo Lagunes Garcia, Lucia Prieto Santamaria et al.

The increasing availability of biological data is improving our understanding of diseases and providing new insight into their underlying relationships. Thanks to the improvements on both text mining techniques and computational capacity, the combination of biological data with semantic information obtained from medical publications has proven to be a very promising path. However, the limitations in the access to these data and their lack of structure pose challenges to this approach. In this document we propose the use of Wikipedia - the free online encyclopedia - as a source of accessible textual information for disease understanding research. To check its validity, we compare its performance in the determination of relationships between diseases with that of PubMed, one of the most consulted data sources of medical texts. The obtained results suggest that the information extracted from Wikipedia is as relevant as that obtained from PubMed abstracts (i.e. the free access portion of its articles), although further research is proposed to verify its reliability for medical studies.

Massimiliano Zanin, David Perez, Kumardev Chatterjee et al.

This paper reports on work performed in the context of the COMPASS SESAR-JU WP-E project, on developing an approach for identifying and filtering inaccurate trajectories (ghost flights) in historical data originating from the EUROCONTROL-operated Demand Data Repository (DDR).