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On Skyline Queries and How to Choose from Pareto Sets

  • Christoph Lofi
  • Wolf-Tilo Balke
Part of the Intelligent Systems Reference Library book series (ISRL, volume 36)

Abstract

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 dimensionality” 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 choosing the most interesting objects from the multitude of skyline objects in order to obtain truly manageable and personalized query results.

Keywords

Skyline Query Skyline Point Subspace Analysis Very Large Data Base Skyline Computation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Technische Universität BraunschweigBraunschweigGermany

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