This book provides a comprehensive compendium of recent research on business intelligence-oriented patent data analysis and mining. Through the book, the readers will gain an essential understanding of the following topics: (1) text mining modeling for patent documents, including statistics modeling and key phrase extraction mining; (2) the patent retrieval method, including chuck based retrieval and retrieval fusion method; and (3) integrated business solutions for stock dynamics, technology prospecting, and minimizing legal exposure. This book provides an informative and insightful reference…mehr
This book provides a comprehensive compendium of recent research on business intelligence-oriented patent data analysis and mining. Through the book, the readers will gain an essential understanding of the following topics: (1) text mining modeling for patent documents, including statistics modeling and key phrase extraction mining; (2) the patent retrieval method, including chuck based retrieval and retrieval fusion method; and (3) integrated business solutions for stock dynamics, technology prospecting, and minimizing legal exposure. This book provides an informative and insightful reference guide for researchers who are newcomers to patent data mining and business intelligence, as well as for professionals and practitioners from industry. Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Dr. Bo Jin is an Associate Professor in Dalian University of Technology. He received his Ph.D. in Computer Science in 2009. His general area of research is data mining and knowledge discovery. He has published prolifically in refereed journals and conference proceedings (60+ papers), e.g., SIGKDD, ICDM, and PAKDD. He has served regularly in the program committees of a number of conferences and is a reviewer for the leading academic journals in his fields, e.g., SIGKDD, ICDM, DASFAA, SDM, TKDE, and SpringPlus. He is a senior member of ACM, IEEE, and CCF. Dr. Hui Xiong received his Ph.D. in Computer Science from the University of Minnesota - Twin Cities, USA, in 2005, the B.E. degree in Automation from the University of Science and Technology of China (USTC), Hefei, China, and the M.S. degree in Computer Science from the National University of Singapore (NUS), Singapore. He is currently a Professor and Vice Chair in the Management Science and Information Systems Department, and the Director of Rutgers Center for Information Assurance, at Rutgers, the State University of New Jersey, where he received a two-year early promotion/tenure (2009), the Rutgers University Board of Trustees Research Fellowship for Scholarly Excellence (2009), the ICDM-2011 Best Research Paper Award (2011), an IBM ESA Innovation Award (2008), the Junior Faculty Teaching Excellence Award (2007), the Junior Faculty Research Award (2008), and Dean's Award for Meritorious Research (2010, 2011, 2013) at Rutgers Business School. Dr. Xiong's general area of research is data and knowledge engineering, with a focus on developing effective and efficient data analysis techniques for emerging data intensive applications. His research has been supported in part by the National Science Foundation (NSF), IBM Research, SAP Corporation, Panasonic USA Inc., Awarepoint Corp., Citrix Systems Inc., and Rutgers University. He has published prolifically in refereed journals and conference proceedings, such as IEEE Transactions on Knowledge and Data Engineering, the VLDB Journal, INFORMS Journal on Computing, Machine Learning, the Data Mining and Knowledge Discovery Journal, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), SIAM International Conference on Data Mining (SDM), IEEE International Conference on Data Mining (ICDM), and ACM International Symposium on Advances in Geographic Information Systems (ACM GIS). He is a co-Editor-in-Chief of Encyclopedia of GIS (Springer, 2008) and an Associate Editor of IEEE Transactions on Data and Knowledge Engineering (TKDE), IEEE Transactions on Big Data (TBD), ACM Transactions on Knowledge Discovery from Data (TKDD) and ACM Transactions on Management Information Systems (TMIS). He has served regularly on the organization and program committees of numerous conferences, including as a Program Co-Chair of the Industrial and Government Track for the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), a Program Co-Chair for the IEEE 2013 International Conference on Data Mining (ICDM), and a General Co-Chair for the IEEE 2015 International Conference on Data Mining (ICDM). He is an ACM Distinguished Scientist and a senior member of the IEEE.
Inhaltsangabe
1. Introduction1.1 Intellectual Property and Patent Mining1.2 Challenges and Progresses1.3 From Patent Mining to Business Intelligence1.4 Overview of the BookReferences2. Survey of Patent Mining2.1 Introduction2.2 Patent Analysis2.2.1 Patent Statistics and Visualization2.2.2 Patent Retrieval2.2.3 Patent Translate2.3 Patent Mining2.3.1 Patent Classification2.3.2 Patent Recommendation2.3.3 Patent Prediction2.4 Tools for Patent Analysis and Mining2.4.1 Preprocessing2.4.2 Analysis and Visualization2.4.3 Patent Map2.5 DiscussionReferences3. Text Mining Methods for Patent3.1 Introduction3.2 Concepts and Data Description3.3 Sememe Statistics Modeling3.4 Key-Phrase Extraction3.5 Patent Classification3.6 Performance Evaluation3.7 Related Work3.8 DiscussionReferences4. Patent Retrieval Methods4.1 Introduction4.2 Chunk-based Modeling4.3 Textual Chunk Retrieval4.4 Retrieval Fusion Modeling4.5 Performance Evaluation4.6 Related Work4.7 DiscussionReferences5. Patent Mining Application for Business Intelligence5.1 Introduction5.2 Patent Activities on Stock Dynamics5.3 Patent Mining for Technology Prospecting5.4 Patent Recommendation for Minimizing Legal Exposure5.5 Performance Evaluation5.6 Related Work5.7 DiscussionReferences6. Conclusion6.1 Promising Topics6.2 The ProspectsAppendix
