Managing and Mining Uncertain Data, a survey with chapters by a variety of well known researchers in the data mining field, presents the most recent models, algorithms, and applications in the uncertain data mining field in a structured and concise way. This book is organized to make it more accessible to applications-driven practitioners for solving real problems. Also, given the lack of structurally organized information on this topic, Managing and Mining Uncertain Data provides insights which are not easily accessible elsewhere. Managing and Mining Uncertain Data is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a reference book for advanced-level students in computer science and engineering, as well as the ACM, IEEE, SIAM, INFORMS and AAAI Society groups.

An Introduction to Uncertain Data Algorithms and Applications.- Models for Incomplete and Probabilistic Information.- Relational Models and Algebra for Uncertain Data.- Graphical Models for Uncertain Data.- Trio A System for Data Uncertainty and Lineage.- MayBMS A System for Managing Large Probabilistic Databases.- Uncertainty in Data Integration.- Sketching Aggregates over Probabilistic Streams.- Probabilistic Join Queries in Uncertain Databases.- Indexing Uncertain Data.- Querying Uncertain Spatiotemporal Data.- Probabilistic XML.- On Clustering Algorithms for Uncertain Data.- On Applications of Density Transforms for Uncertain Data Mining.- Frequent Pattern Mining Algorithms with Uncertain Data.- Probabilistic Querying and Mining of Biological Images.