Abstract
Data incompleteness becomes a frequent phenomenon in contemporary non-trivial database applications such as web autonomous databases, incomplete databases, big data and crowd-sourced mobile databases. Processing queries over these incomplete databases impose several challenges that negatively influence processing the queries. Most importantly, the query results derived from incomplete databases are also incomplete as certain values of the query result are not present. Result incompleteness may lead to misguiding the user in multi-criteria decision-making and decision support systems. Skyline queries are one of the most prominent queries applied over these recommendation and decision-making systems. Most recently, several studies have suggested exploiting the crowd-sourced databases in order to estimate the missing values by generating plausible substitute values using the crowd resources. Crowd-sourced databases have proved to be a powerful solution to perform user-given tasks by integrating human intelligence and experience to process the tasks. However, task processing using crowd-sourced platform incurs additional monetary cost and increases the time latency. Also, it is not always possible to produce a satisfactory result according to the user’s preferences. Thus, an efficient and cost-effective approach for estimating the missing values of the skylines on crowd-sourced enabled incomplete databases is necessary which is achieved by exploiting the available data and the implicit relationships in the database before referring to the crowd is needed