Skyline queries are well known for their intuitive query formalization and easy to understand semantics when selecting the most interesting database objects in a personalized fashion. They naturally fill the gap between set-based SQL queries and rank-aware database retrieval and thus have emerged in the last few years as a popular tool for personalized retrieval in the database research community. Unfortunately, the Skyline paradigm also exhibits some significant drawbacks. Most prevalent among those problems is the so called “curse of di-mensionality” which often leads to unmanageable result set sizes. This flood of query results, usually containing a significant portion of the original database, in turn severely hampers the paradigm‟s applicability in real-life systems. In this chapter, we will provide a survey of techniques to remedy this problem by choos-ing the most interesting objects from the multitude of skyline objects in order to obtain truly manageable and personalized query results.