Abstract. The internet has been evolving from a communication media to an environment where users talk about the most diverse topics, reflecting the dynamics of the society at broad. Characterizing and understanding how the internet data may be used for assessing real events becomes a key component of many Internet-based applications and demands the development of new data mining models and techniques. Data mining in such scenarios is challenging because the data is intrinsically uncertain and multi-scale, the patterns to be mined are complex and evolve through time, and there is a huge amount of information that need to be processed in real time. In this talk we present a framework for the research and development of data mining models, algorithms and systems that target these challenging scenarios. We also present the Web Observatory, a platform for collecting, analyzing and presenting, at real time, information mined from social networks and the web, as well as some of its instances that focused on sports, politics, and health.

Bio. Wagner Meira Jr. obtained his PhD in Computer Science from the University of Rochester in 1997 and is currently Professor at the Computer Science Department at Universidade Federal de Minas Gerais, Brazil. He is also the lead researcher of the Knowledge Discovery research group of INWeb. His research focuses on data mining models and algorithms, their parallelization, and application to areas such as information retrieval, bioinformatics, and e-governance, as well as on scalability and efficiency of large scale parallel and distributed systems, from massively parallel to Internet-based platforms.