  Segregation is the separation of social groups in the physical or in the
online world. Segregation discovery consists of finding contexts of
segregation. In the modern digital society, discovering segregation is
challenging, due to the large amount and the variety of social data. We present
a tool in support of segregation discovery from relational and graph data. The
SCube system builds on attributed graph clustering and frequent itemset mining.
It offers to the analyst a multi-dimensional segregation data cube for
exploratory data analysis. The demonstration first guides the audience through
the relevant social science concepts. Then, it focuses on scenarios around case
studies of gender occupational segregation. Two real and large datasets about
the boards of directors of Italian and Estonian companies will be explored in
search of segregation contexts. The architecture of the SCube system and its
computational efficiency challenges and solutions are discussed.
