The Hexagon Algorithm for Pareto Preference Queries
T. Preisinger, W. Kießling
Abstract:
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.
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