Bøger af Hongzhi Wang

This book consists of selected and peer-reviewed papers presented at 2022 4th International Conference on Big Data Engineering and Technology (BDET), held during April 22-24, 2022, in Singapore. As IT infrastructure and data management technologies have become critical assets and capabilities for today's enterprises, this book aims to be part of the effort in contributing to their development. In particular, the BDET conference series aims to provide the much needed forum for researchers and practitioners across the world who are actively engaged in advancing research and raising awareness of the many challenges in the diverse field of big data engineering and technology to share their research outcomes and bounce ideas off their international colleagues. Over the last few years, the conference series has brought together the latest developments of novel theory in big data, algorithm and applications, emerging standards for big data, big data infrastructure, MapReduce and cloud computing, big data visualization, big data curation and management, big data semantics, scientific discovery and intelligence, which collectively form parts of the cyber-physical systems of interest. It is hoped that the book will prove useful to students, researchers, and professionals working in the field of big data engineering and applications in cyber-physical systems.

This book addresses several knowledge discovery problems on multi-sourced data where the theories, techniques, and methods in data cleaning, data mining, and natural language processing are synthetically used. This book mainly focuses on three data models: the multi-sourced isomorphic data, the multi-sourced heterogeneous data, and the text data. On the basis of three data models, this book studies the knowledge discovery problems including truth discovery and fact discovery on multi-sourced data from four important properties: relevance, inconsistency, sparseness, and heterogeneity, which is useful for specialists as well as graduate students. Data, even describing the same object or event, can come from a variety of sources such as crowd workers and social media users. However, noisy pieces of data or information are unavoidable. Facing the daunting scale of data, it is unrealistic to expect humans to "e;label"e; or tell which data source is more reliable. Hence, it is crucial to identify trustworthy information from multiple noisy information sources, referring to the task of knowledge discovery. At present, the knowledge discovery research for multi-sourced data mainly faces two challenges. On the structural level, it is essential to consider the different characteristics of data composition and application scenarios and define the knowledge discovery problem on different occasions. On the algorithm level, the knowledge discovery task needs to consider different levels of information conflicts and design efficient algorithms to mine more valuable information using multiple clues. Existing knowledge discovery methods have defects on both the structural level and the algorithm level, making the knowledge discovery problem far from totally solved.