Ranking the sky: Discovering the importance of skyline points through subspace dominance relationships

摘要: Skyline queries aim to help users make intelligent decisions over complex data by discovering a set of interesting points, when different and often conflicting criteria are considered. Unfortunately, as the dimensionality dataset grows, skyline operator loses its discriminating power returns large fraction data. The huge size result hinders decision-making motivates ranking points. Therefore, prefer retrieve top-k points instead whole set. In this paper, we propose SKYRANK, framework for in absence user-defined preference function, thereby limited subset most For purpose, define graph, which relies on dominance relationships between subsets dimensions (subspaces). SKYRANK applies well-known authority-based algorithms graph and, described discovers importance point exploiting subspace relationships. Furthermore, extend handle queries, user's preferences available. Our experimental evaluation illustrates complexity ability our framework.