Katja Hose
ABSTRACT
During the last decade, data management and storage have become increasingly distributed. In consideration of the huge amount of data available in such systems, advanced query operators, such as skyline queries, are necessary to help users process the data. For example, a user who is interested in buying a car wants to find a good trade-off between minimum age and minimum price. It is not obvious how much cheaper a car should be, if it is one year older than another car. Thus, the skyline query will retrieve a set of data items that are the best trade-offs for the user's preferences. The skyline operator has been proposed about a decade ago, but research on skyline queries, especially in distributed scenarios, is still an ongoing process.
Query processing in distributed environments poses inherent challenges and requires non-traditional techniques due to the distribution of content and the lack of global knowledge. In this tutorial, we will outline the objectives and the main principles that any distributed skyline approach has to fulfill, leading to useful guidelines for the design of efficient distributed skyline algorithms. More importantly, distributed processing of other query types share the same objectives and principles, therefore several of the guidelines are applicable also for other query types. Furthermore, this tutorial will provide a broad survey of the state-of-the-art in distributed skyline processing, present a categorization of the existing approaches based on their characteristics, and point out open research challenges in distributed skyline processing.
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Index Terms
- Distributed skyline processing: a trend in database research still going strong
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