Knowledge Discovery from Multi-Sourced Data
Chen Ye · Hongzhi Wang · Guojun Dai
thg 6 2022 · Springer Nature
Sách điện tử
83
Trang
reportĐiểm xếp hạng và bài đánh giá chưa được xác minh Tìm hiểu thêm
Giới thiệu về sách điện tử này
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 “label” 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.
Giới thiệu tác giả
Chen Ye is currently an Associate Researcher at the School of Computer Science and Technology, Hangzhou Dianzi University, China. She received the Ph.D. degree in Computer Software and Theory from Harbin Institute of Technology, China. Her current research interests include data repairing, truth discovery, and crowdsourcing. She has won the ACM SIGMOD China Doctoral Dissertation Award in 2020.
Hongzhi Wang is a Professor and Doctoral Supervisor at the School of Computer Science and Technology, Harbin Institute of Technology, China. His research interests include big data management and analysis, data quality, graph data management, and web data management. He has published more than 150 papers, and he is the Primary Investigator of more than 10 projects including three NSFC projects, and co-PI of 973, 863, and NSFC key projects. He was awarded as Microsoft fellowship, China Excellent Database Engineer, and IBM Ph.D. fellowship.
Guojun Dai is now working in the School of Computer Science and Technology of Hangzhou Dianzi University, as the Head of the National Brain-Computer Collaborative Intelligent Technology International Joint Research Center, the director of the Institute of Computer Application Technology. His research interests include Internet of Things, industrial big data, network collaborative manufacturing, edge computing, brain-computer interface, cognitive computing, artificial intelligence. He has published over 50 research papers in top-quality international conferences and journals, particularly, INFOCOM, IEEE Transactions on Industrial Informatics, and IEEE Transactions on Mobile Computing.
Hongzhi Wang is a Professor and Doctoral Supervisor at the School of Computer Science and Technology, Harbin Institute of Technology, China. His research interests include big data management and analysis, data quality, graph data management, and web data management. He has published more than 150 papers, and he is the Primary Investigator of more than 10 projects including three NSFC projects, and co-PI of 973, 863, and NSFC key projects. He was awarded as Microsoft fellowship, China Excellent Database Engineer, and IBM Ph.D. fellowship.
Guojun Dai is now working in the School of Computer Science and Technology of Hangzhou Dianzi University, as the Head of the National Brain-Computer Collaborative Intelligent Technology International Joint Research Center, the director of the Institute of Computer Application Technology. His research interests include Internet of Things, industrial big data, network collaborative manufacturing, edge computing, brain-computer interface, cognitive computing, artificial intelligence. He has published over 50 research papers in top-quality international conferences and journals, particularly, INFOCOM, IEEE Transactions on Industrial Informatics, and IEEE Transactions on Mobile Computing.
Xếp hạng sách điện tử này
Cho chúng tôi biết suy nghĩ của bạn.
Đọc thông tin
Điện thoại thông minh và máy tính bảng
Cài đặt ứng dụng Google Play Sách cho Android và iPad/iPhone. Ứng dụng sẽ tự động đồng bộ hóa với tài khoản của bạn và cho phép bạn đọc trực tuyến hoặc ngoại tuyến dù cho bạn ở đâu.
Máy tính xách tay và máy tính
Bạn có thể nghe các sách nói đã mua trên Google Play thông qua trình duyệt web trên máy tính.
Thiết bị đọc sách điện tử và các thiết bị khác
Để đọc trên thiết bị e-ink như máy đọc sách điện tử Kobo, bạn sẽ cần tải tệp xuống và chuyển tệp đó sang thiết bị của mình. Hãy làm theo hướng dẫn chi tiết trong Trung tâm trợ giúp để chuyển tệp sang máy đọc sách điện tử được hỗ trợ.
