Identifying Top k Dominating Objects over Uncertain Data

Liming Zhan, Ying Zhang, Wenjie Zhang, Xuemin Lin

Abstract: 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, and many important queries are re-investigated in the context of uncertain data models. In the paper, we study the problem of top k dominating query on multi-dimensional uncertain objects, which is an essential method in the multi-criteria decision analysis when an explicit scoring function is not available. Particularly, we formally introduce the top k dominating model based on the state-of-the-art top k semantic over uncertain data. We also propose effective and efficient algorithms to identify the top k dominating objects. Novel pruning techniques are proposed by utilizing the spatial indexing and statistic information, which significantly improve the performance of the algorithms in terms of CPU and I/O costs. Comprehensive experiments on real and synthetic datasets demonstrate the effectiveness and efficiency of our techniques.