A Model for Processing Skyline Queries in Crowd-sourced Databases

摘要: Nowadays, in most of the modern database applications, lots critical queries and tasks cannot be completely addressed by machine. Crowd-sourcing has become a new paradigm for harness human cognitive abilities to process these computer hard tasks. In particular, those problems  that are difficult machines but easier humans can solved better than ever, such as entity resolution, fuzzy matching predicates joins, image recognition. Additionally, crowd-sourcing allows performing operators on incomplete data workers involved provide estimated values during run-time. Skyline which received formidable attention community last decade, exploited variety applications multi-criteria decision making support systems. Various works have been accomplished address issues skyline query database. This includes with full partial complete data. However, we argue processing not an appropriate attention. Therefore, efficient approach is needed. paper attempts present model tackling issue The main idea proposed exploiting available estimate missing values. Besides, tries explore crowd-sourced order more accurate results, when local failed precise ensure high quality result could obtained, certain factors should considered worker selection carry out task monetary cost. Other time latency generate results.