The skyline operator (aka Pareto front) extracts relevant records from multidimensional databases according to multiple criteria. This operator has received a lot of attention because of its ability to identify the best records in a database without requiring to specify complex parameters like the relative importance of each criterion (as it is done in ranking methods). However, recent attempts to apply the operator to real data analysis tasks have revealed some weaknesses of the original definition.

In this presentation I will introduce the skyline operator, indicate some recent research trends related to these weaknesses and focus on the so-called aggregate skyline queries, where the skyline is executed on sets of records instead of single items. This operator can be used to express queries in the form: return the best groups depending on the features of their elements, and thus provides a powerful combination of grouping and skyline functionality.

I will conclude the presentation by showing an application of the skyline operator to complex data representing multiple social networks.