Identifying Top k Dominating Objects over Uncertain Data

摘要: Uncertainty is inherent in many important applications, such as data integration, environmental surveillance, location-based services (LBS), sensor monitoring and radio-frequency identification (RFID). In recent years, we have witnessed significant research efforts devoted to producing probabilistic database management systems, queries are re-investigated the context of uncertain models. paper, study problem top k dominating query on multi-dimensional objects, which an essential method multi-criteria decision analysis when explicit scoring function not available. Particularly, formally introduce model based state-of-the-art semantic over data. We also propose effective efficient algorithms identify objects. Novel pruning techniques proposed by utilizing spatial indexing statistic information, significantly improve performance terms CPU I/O costs. Comprehensive experiments real synthetic datasets demonstrate effectiveness efficiency our techniques.