Bernhard Pfahringer

Bernhard Pfahringer received his PhD degree from the University of Technology in Vienna, Austria, in 1995. He is currently a Professor with the Department of Computer Science at the University of Waikato in New Zealand. His interests span a range of data mining and machine learning sub-fields, with a focus on streaming, randomization, and complex data.

Current challenges in stream mining

Stream mining is concerned with online learning from non-stationary data sources. I will argue that many, if not all, big data mining endeavours are instances of stream mining. This presentation will highlight issues in stream mining, including proper evaluation, temporal dependencies, label acquisition, and preprocessing, and will present some preliminary solutions for these challenges.

Marko Grobelnik

Marko Grobelnik is an expert in the areas of analysis of large amounts of complex data with the purpose to extract useful knowledge. In particular, the areas of expertise comprise: Data Mining, Text Mining, Information Extraction, Link Analysis, and Data Visualization as well as more integrative areas such as Semantic Web, Knowledge Management and Artificial Intelligence. Apart from research on theoretical aspects of data analysis techniques he has considerable experience in the field of practical applications and development of business solutions based on the innovative technologies.