Due to a variety of reasons including data randomness and incompleteness, noise, privacy, etc., uncertainty is inherent in many important applications, such as location-based services (LBS), sensornetwork monitoring and radio-frequency identification (RFID). Recently, considerable research efforts have been devoted into the field of uncertainty-aware spatial query processing such that the uncertainty of the data can be effectively and efficiently tackled. In this paper, we study the problem of finding top k most influential facilities over a set of uncertain objects, which is an important and fundamental spatial query in the above applications. Based on the maximal utility principle, we propose a new ranking model to identify the top k most influential facilities, which carefully captures influence of facilities on the uncertain objects.
By utilizing two uncertain object indexing techniques, R-tree and U-Quadtree, effective and efficient algorithms are proposed following the filtering and verification paradigm, which significantly improves the performance of the algorithms in terms of CPU and I/O costs. To effectively support uncertain objects with a large number of instances, we also develop randomized algorithms with accuracy guarantee. Then a hybrid algorithm is devised which effectively combines the randomized and exact algorithms. Comprehensive experiments on real datasets demonstrate the effectiveness and efficiency of our techniques.