Skyline Snippets

摘要

There is a strong demand for a deep personalization of search systems for many Internet applications. In this respect the proper handling of user preferences plays an important role. Here we focus on the efficient evaluation of the Pareto preference operator for structured data in very large databases. The result set of such a Pareto query, also known as the "skyline", tends to become very large for higher dimensionalities. Often it is too time-consuming or just not necessary to compute the entire skyline, instead only some fraction of it, called a "snippet", is sufficient. In this paper we contribute a novel algorithm for a fast computation of such skyline snippets. Our solutions do not rely on the availability of specialized pre-computed indexes, hence are generally applicable. We demonstrate the performance of our approach by several benchmarks studies. The presented results suggest that even for complex Pareto queries, yielding very large skylines, snippets can be computed sufficiently fast, and therefore can be integrated into online Web services.