Output-sensitive Evaluation of Prioritized Skyline Queries

Skylines assume that all attributes are equally important, as each dimension can always be traded off for another. Prioritized skylines (p-skylines) take into account non-compensatory preferences, where some dimensions are deemed more important than others, and trade-offs are constrained by the relative importance of the attributes involved.

In this paper we show that querying using non-compensatory preferences is computationally efficient. We focus on preferences that are representable with p-expressions, and develop an efficient in-memory divide-and-conquer algorithm for answering p-skyline queries. Our algorithm is output-sensitive; this is very desirable in the context of preference queries, since the output is expected to be, on average, only a small fraction of the input. We prove that our method is well behaved in both the worst- and the average-case scenarios. Additionally, we develop a general framework for benchmarking p-skyline algorithms, showing how to sample prioritized preference relations uniformly, and how to highlight the effect of data correlation on performance. We conclude our study with extensive experimental results.