2nd edition of the Big Data Talent Awards

Below you can check the winners and finalists of the 2nd edition of the Big Data Talent Awards:

Winners of the Big Data Talent Awards 2016

Category I : final degree project or end of master's degree or postgraduate

Tourist Factory (UPC) (Authors: Beatriz martin, Juan Pablo López, Yulia Zvyagelskaya and Rodica Fazakas). Tourist Factory is a real-time AaaS (Analyticsasaservice) solution that, on the one hand, identifies tourists to social networks in large cities and, on the other hand, implements a recommendation system based on social content and aimed at these tourists.

 Category II: doctoral thesis

Large-scale comparative bioinformatics analyzes (UPF carried out at the Center for Genomic Regulation) (Author: Maria Chatzou). The aim of this thesis has been to explore the impact of large-scale data analysis on multiple sequence alignment and phylogenetic reconstruction, two of the most popular modeling methods in biology.

Finalists of the Big Data Talent Awards 2016

Category I : final degree project or end of master's degree or postgraduate

Convector - Public transport optimization through Big Data analytics (UPC) (Authors: Albert Quiroga, Ferran Cabezas, Xavier Mas and Carles Teixidó). The main objective of this project is to design, build and implement a Big Data information analysis system that analyzes the data generated for a public transport company in order to extract valuable insights for the business.

Computational Framework for the Assessment of New Forms of Organization in Social Media (UNED) (Author: Pablo Aragón). This project proposes a computational framework to better determine and analyze the Twitter networks of political parties using community detection algorithms.

Category II: doctoral thesis

Evolutionary bags of space-time analysis for human analysis (UB) (Author: Victor Ponce). The thesis analyzes and presents different approaches for machine learning of space-time representations from data from multiple devices, emphasizing visual data and artificial vision techniques. Mainly evolutionary computing methods, dynamic programming systems and generative models are defined and used. The objective is the analysis of human behavior through the recognition of gestural patterns and actions.

Prediction of protein and nucleic acid interactions (UPF) (Author: Davide Cirillo). The purpose of this thesis has been the development of high-throughput bioinformatics methods to quantitatively assess associations between proteins and nucleic acids (NA).