Optimizing Skyline Query Processing in Incomplete Data

摘要: Given the significance of skyline queries, they are incorporated in various modern applications including personalized recommendation systems as well decision-making and decision-support systems. Skyline queries used to identify superior data items database. Most previously proposed algorithms work on a complete database where always present (non-missing). However, many contemporary real-world databases, particularly those databases with large cardinality high dimensionality, such assumption is not necessarily valid. Hence, missing pose new challenges if processing cannot easily apply methods that designed for data. This due fact imperfect cause loss transitivity property method cyclic dominance . paper presents framework called Optimized Incomplete (OIS) which utilizes technique simplifies process helps prune before performing process. The strategy assures number domination tests significantly reduced. A set experiments has been accomplished using both real synthetic datasets aimed at validating performance framework. experiment results confirm OIS indeed steadily outperforms current approaches terms required retrieve skylines.