Author Description

--> Data analysis is of upmost importance in the mining of big data, where knowledge discovery and inference are the basis for intelligent systems to support the real world applications. However, the process involves knowledge acquisition, representation, inference and data, Bayesian network (BN) is the key technology plays a key role in knowledge representation, in order to pave way to cope with incomplete, fuzzy data to solve the real-life problems. This book presents Bayesian network as a technology to support data-intensive and incremental learning in knowledge discovery, inference and data fusion in uncertain environment. --> Contents: Introduction Data-Intensive Learning of Uncertain Knowledge Data-Intensive Inferences of Large-Scale Bayesian Networks Uncertain Knowledge Representation and Inference for Lineage Processing over Uncertain Data Uncertain Knowledge Representation and Inference for Tracing Errors in Uncertain Data Fusing Uncertain Knowledge in Time-Series Data Summary --> --> Readership: Graduate students, researchers and professionals in the field of artificial intelligence/machine learning and information sciences, especially in databases. --> Keywords:Uncertain Knowledge;Bayesian Network;Data-Intensive Computing;Lineage;Inference;FusionReview: Key Features: Upon the preliminaries of BN (Pearl, 1988), this book establishes the connection between massive/uncertain/dynamic data management and uncertainty in artificial intelligence, specifically taking BN as the knowledge framework; different from the publications (Pearl, 1988; Russel & Norvig, 2010), this book concerns uncertain knowledge representation and corresponding inferences from the data-driven perspective, where we focus on the construction of knowledge models with respect to specific applications; different from the publicatio