Data mining and knowledge discovery can be considered today as mature research fields with numerous algorithms and studies to extract knowledge from data in different forms. Although, most existing data mining approaches look for patterns in tabular data, there are also numerous studies which already look for patterns in complex data (e.g. multi-table data, XML data, web data, time series and sequences, graphs and trees).

The recent developments in technologies and life sciences have paved the way to the proliferation of data collections representing new complex interactions between entities in distributed and heterogeneous sources. These interactions may be spanned at multiple levels of granularity as well as at spatial and temporal dimensions.

The purpose of this workshop is to bring together researchers and practitioners of data mining interested in exploring emerging technologies and applications where complex patterns in expressive languages are principally extracted from new prominent data sources like blogs, event or log data, biological data, spatio-temporal data, social networks, mobility data, sensor data and streams, and so on. We are interested in advanced techniques which preserve the informative richness of data and allow us to efficiently and efficaciously identify complex information units present in such data.