Discovery and Fusion of Uncertain Knowledge in Data

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.
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 publication (Han, 2011), this book focuses on the critical problem of knowledge engineering specially taking BN as the framework, instead of the previously-unknown patterns by mining data
• This book presents the theoretic conclusions, algorithmic strategies, running examples and empirical studies while emphasizing the soundness in both theoretic/semantic and executive/applicable perspectives of the methods for discovery and fusion of uncertain knowledge in data
• This book is appropriately a reference book for researchers in the fields of massive data analysis, artificial intelligence and knowledge engineering. As well, this book can be also adopted as textbook for graduate students who major in data mining and knowledge discovery, or intelligent data analysis etc.