Uncertainty is inherent in many novel and important applications such as market surveillance, information extraction sensor data analysis, etc. In the recent a few decades, uncertain data has attracted considerable research attention. There are various factors that cause the uncertainty, for instance randomness or incompleteness of data, limitations of equipment and delay or loss in data transfer. A probabilistic threshold range aggregate (PRTA) query retrieves summarized information about the uncertain objects in the database satisfying a range query, with respect to a given probability threshold. This thesis is trying to address and handle this important type of query which there is no previous work studying on. We formulate the problem in both discrete and continuous uncertain data model and develop a novel index structure, asU-tree (aggregate-based sampling-auxiliary U-tree) which not only supports exact query answering but also provides approximate results with accuracy guarantee if efficiency is more concerned. The new asU-tree structure is totally dynamic. Query processing algorithms for both exact answer and approximate answer based on this new index structure are also proposed. An extensive experimental study shows that asU-tree is very efficient and effective over real and synthetic datasets.

Uncertainty is inherent in many novel and important applications such as market surveillance, information extraction sensor data analysis, etc. In the recent a few decades, uncertain data has attracted considerable research attention. There are various factors that cause the uncertainty, for instance randomness or incompleteness of data, limitations of equipment and delay or loss in data transfer. A probabilistic threshold range aggregate (PRTA) query retrieves summarized information about the uncertain objects in the database satisfying a range query, with respect to a given probability threshold. This thesis is trying to address and handle this important type of query which there is no previous work studying on. We formulate the problem in both discrete and continuous uncertain data model and develop a novel index structure, asU-tree (aggregate-based sampling-auxiliary U-tree) which not only supports exact query answering but also provides approximate results with accuracy guarantee if efficiency is more concerned. The new asU-tree structure is totally dynamic. Query processing algorithms for both exact answer and approximate answer based on this new index structure are also proposed. An extensive experimental study shows that asU-tree is very efficient and effective over real and synthetic datasets.