Skyline queries in crowd-enabled databases

摘要: Skyline queries are a well-established technique for database query personalization and widely acclaimed their intuitive formulation mechanisms. However, when operating on incomplete datasets, skylines severely hampered often have to resort highly error-prone heuristics. Unfortunately, datasets frequent phenomenon, especially generated automatically using various information extraction or integration approaches. Here, the recent trend of crowd-enabled databases promises powerful solution: during execution, some operators can be dynamically outsourced human workers in exchange monetary compensation, therefore enabling elicitation missing values runtime. this feature heavily impacts response times (monetary) execution costs. In paper, we present an innovative hybrid approach combining dynamic crowd-sourcing with heuristic techniques order overcome current limitations. We will show that by assessing individual risk tuple poses respect overall result quality, efforts eliciting narrowly focused only those tuples may degenerate expected quality most strongly. This leads algorithm computing skyline sets data maximum while optimizing