Computing Skyline Incrementally in Response to Online Preference Modification
Résumé
Skyline queries retrieve the most interesting objects from a database with respect to multi-dimensional preferences. Identifying and extracting the relevant data corresponding to multiple criteria provided by users remains a difficult task, especially when the data are large. In 2008-2009, Wong et al. showed how to avoid costly skyline query computations by deriving the skyline points associated with any preference from the skyline points associated with the most preferred values. They propose to materialize these points in a structure called IPO-tree (Implicit Preference Order Tree). However, its size is exponential with respect to the number of dimensions. We propose an incremental method for calculating the skyline points related to several dimensions associated with dynamic preferences. For this purpose, a materialization of linear size which allows a great flexibility for dimension preference updates is defined. This contribution improves notably the computation cost of queries. Experiments on synthetic data highlight the relevance of EC 2 Sky compared to IPO-Tree.