Skyline Queries over Incomplete Data - Error Models for Focused Crowd-Sourcing

摘要: Skyline queries are a well-known technique for explorative retrieval, multi-objective optimization problems, and personalization tasks in databases. They widely acclaimed their intuitive query formulation mechanisms. However, when operating on incomplete datasets, skyline processing is severely hampered often has to resort error-prone heuristics. Unfortunately, datasets frequent phenomenon due widespread use of automated information extraction aggregation. In this paper, we evaluate compare various established heuristics adapting skylines focusing specifically the error they impose result. Building upon these results, argue improving result quality by employing crowd-enabled This allows dynamic outsourcing some database operators human workers, therefore enabling elicitation missing values during runtime. each crowd-sourcing operation will monetary runtime costs. Therefore, our main contribution introducing sophisticated model, allowing us concentrate those tuples that highly likely be error-prone, while relying safer tuples. focused strike perfect balance between costs result's quality.