Skyline queries are well-known for their intuitive query formalization and easy to understand semantics for selecting the most interesting data objects from large data sets. They naturally fill the gap between set-based queries using strict predicates and only few personalization options and rank-aware database retrieval, offering a high degree of personalization at the cost of very complex query formalization. Thus, skyline queries enjoyed popularity in the database personalization research community. Unfortunately, the simplicity and elegance of the query paradigm come at high costs: skyline queries often suffer from a problem usually known as “curse of dimensionality”. With the increasing number of query attributes, the size of skyline result sets grows exponentially and the results are thus hardly useful or managea-ble by users. This problem severely hinders the practical applica-tion of the skyline paradigm.
In this paper, the concept of trade-offs skylines is proposed as a natural extension to the skyline paradigm which is specifically designed as a remedy for the curse of dimensionality.