Incremental Computation of Skyline Queries with Dynamic Preferences

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