The Hexagon Algorithm for Pareto Preference Queries

Database queries expressing user preferences have been found to be crucial for personalized applications. Such preference queries, in particular Pareto preference queries, pose new optimization challenges for efficient evaluation. So far however, all known generic Pareto evaluation algorithms suffer from non-linear worst case runtimes. Here we present the first generic algorithm, called Hexagon, with linear worst case complexity for any data distribution under certain reasonable assumptions. In addition, our performance investigations provide evidence that Hexagon also beats competing Block-Nested-Loop style algorithms in the average case. Therefore Hexagon has the potential to become one key algorithm in each preference query optimizer’s repertoire.