Hsinchun Chen

Introduction The description, planning and governance of nanotechnology development require data on knowledge creation and innovation in various areas of application, how these evolve in time and what is the international context. This book aims to selectively provide such information based on the analysis of databases for science and engineering articles (Thompson Citation Index) and patents (USPTO in the United States, EPO in Europe and JPO in Japan). After a survey of the investigative methods, comparative results per countries, technology fields and research organizations are presented for articles and patents in parts of the interval 1976 to 2006. Interesting features on the evolution of major research themes and connection between research awards and patents have been obtained via longitudinal investigation of the published articles and patent data, as well as connection between NSF funding in nanotechnology and patents awarded to their principal investigators. A web-based system has been developed for accessing and visualizing nanotechnology patents, articles and NSF awards. Scope and Organization The monograph aims to present its chapters in a manner understandable and useful to students, researchers, and nanotechnology professionals. The titles of the eleven chapters are listed below: · Chapter 1. Nanotechnology: An Emerging Field · Chapter 2. Knowledge Mapping: Foundation · Chapter 3. Knowledge Mapping: Analysis Framework · Chapter 4. Mapping Nanotechnology Innovations via USPTO Database: A Longitudinal Study, 1976-2002 · Chapter 5. Federal Funding and Nanotechnology Innovations: NSF Funding and USPTO Patent Analysis, 1991-2002 · Chapter 6.

The University of Arizona Artificial Intelligence Lab (AI Lab) Dark Web project is a long-term scientific research program that aims to study and understand the international terrorism (Jihadist) phenomena via a computational, data-centric approach. We aim to collect "ALL" web content generated by international terrorist groups, including web sites, forums, chat rooms, blogs, social networking sites, videos, virtual world, etc. We have developed various multilingual data mining, text mining, and web mining techniques to perform link analysis, content analysis, web metrics (technical sophistication) analysis, sentiment analysis, authorship analysis, and video analysis in our research. The approaches and methods developed in this project contribute to advancing the field of Intelligence and Security Informatics (ISI). Such advances will help related stakeholders to perform terrorism research and facilitate international security and peace. This monograph aims to provide an overview of the Dark Web landscape, suggest a systematic, computational approach to understanding the problems, and illustrate with selected techniques, methods, and case studies developed by the University of Arizona AI Lab Dark Web team members. This work aims to provide an interdisciplinary and understandable monograph about Dark Web research along three dimensions: methodological issues in Dark Web research; database and computational techniques to support information collection and data mining; and legal, social, privacy, and data confidentiality challenges and approaches. It will bring useful knowledge to scientists, security professionals, counterterrorism experts, and policy makers. The monograph can also serve as a reference material or textbook in graduate level courses related to information security, information policy, information assurance, information systems, terrorism, and public policy.

Data mining is the process of extracting hidden patterns from data, and it’s commonly used in business, bioinformatics, counter-terrorism, and, increasingly, in professional sports. First popularized in Michael Lewis’ best-selling Moneyball: The Art of Winning An Unfair Game, it is has become an intrinsic part of all professional sports the world over, from baseball to cricket to soccer. While an industry has developed based on statistical analysis services for any given sport, or even for betting behavior analysis on these sports, no research-level book has considered the subject in any detail until now. Sports Data Mining brings together in one place the state of the art as it concerns an international array of sports: baseball, football, basketball, soccer, greyhound racing are all covered, and the authors (including Hsinchun Chen, one of the most esteemed and well-known experts in data mining in the world) present the latest research, developments, software available, and applications for each sport. They even examine the hidden patterns in gaming and wagering, along with the most common systems for wager analysis.

Computer-based infectious disease surveillance systems are capable of real-time or near real-time detection of serious illnesses and potential bioterrorism agent exposures and represent a major step forward in disease surveillance. Infectious Disease Informatics: Syndromic Surveillance for Public Health and Bio-Defense is an in-depth monograph that analyzes and evaluates the outbreak modeling and detection capabilities of existing surveillance systems under a unified framework, and presents the first book-length coverage of the subject from an informatics-driven perspective. Individual chapters consider the state of the art, including the facilitation of data collection, sharing and transmission; a focus on various outbreak detection methods; data visualization and information dissemination issues; and system assessment and other policy issues. Eight chapters then report on several real-world case studies, summarizing and comparing eight syndromic surveillance systems, including those that have been adopted by many public health agencies (e.g., RODS and BioSense). The book concludes with a discussion of critical issues and challenges, with a look to future directions. This book is an excellent source of current information for researchers in public health and IT. Government public health officials and private-sector practitioners in both public health and IT will find the most up-to-date information available, and students from a variety of disciplines, including public health, biostatistics, information systems, computer science, and public administration and policy will get a comprehensive look at the concepts, techniques, and practices of syndromic surveillance.

The University of Arizona Artificial Intelligence Lab (AI Lab) Dark Web project is a long-term scientific research program that aims to study and understand the international terrorism (Jihadist) phenomena via a computational, data-centric approach. We aim to collect "ALL" web content generated by international terrorist groups, including web sites, forums, chat rooms, blogs, social networking sites, videos, virtual world, etc. We have developed various multilingual data mining, text mining, and web mining techniques to perform link analysis, content analysis, web metrics (technical sophistication) analysis, sentiment analysis, authorship analysis, and video analysis in our research. The approaches and methods developed in this project contribute to advancing the field of Intelligence and Security Informatics (ISI). Such advances will help related stakeholders to perform terrorism research and facilitate international security and peace. This monograph aims to provide an overview of the Dark Web landscape, suggest a systematic, computational approach to understanding the problems, and illustrate with selected techniques, methods, and case studies developed by the University of Arizona AI Lab Dark Web team members. This work aims to provide an interdisciplinary and understandable monograph about Dark Web research along three dimensions: methodological issues in Dark Web research; database and computational techniques to support information collection and data mining; and legal, social, privacy, and data confidentiality challenges and approaches. It will bring useful knowledge to scientists, security professionals, counterterrorism experts, and policy makers. The monograph can also serve as a reference material or textbook in graduate level courses related to information security, information policy, information assurance, information systems, terrorism, and public policy.

On September 11, 2001 the World became cognizant of the security challenges it faces on an international scale. With this awareness a commitment has come from the scientific, engineering, and health communities to help the world meet an array of security challenges. From these activities the science of "Intelligence and Security Informatics" is emerging, which will influence a new generation of policy makers, practitioners, researchers, and students. INTELLIGENCE AND SECURITY INFORMATICS FOR INTERNATIONAL SECURITY: Information Sharing and Data Mining presents a systematic national security research framework, and discusses IT technical components, and directions. The book reflects a decade of leading-edge research on intelligence and security informatics from the Artificial Intelligence Laboratory and the NSF COPLINK Center for Homeland Security Information Technology Research, as well as the application in real-world community situations by the center’s director and the book’s author, Dr. Hsinchun Chen.

Data mining is the process of extracting hidden patterns from data, and it’s commonly used in business, bioinformatics, counter-terrorism, and, increasingly, in professional sports. First popularized in Michael Lewis’ best-selling Moneyball: The Art of Winning An Unfair Game, it is has become an intrinsic part of all professional sports the world over, from baseball to cricket to soccer. While an industry has developed based on statistical analysis services for any given sport, or even for betting behavior analysis on these sports, no research-level book has considered the subject in any detail until now. Sports Data Mining brings together in one place the state of the art as it concerns an international array of sports: baseball, football, basketball, soccer, greyhound racing are all covered, and the authors (including Hsinchun Chen, one of the most esteemed and well-known experts in data mining in the world) present the latest research, developments, software available, and applications for each sport. They even examine the hidden patterns in gaming and wagering, along with the most common systems for wager analysis.

Computer-based infectious disease surveillance systems are capable of real-time or near real-time detection of serious illnesses and potential bioterrorism agent exposures and represent a major step forward in disease surveillance. Infectious Disease Informatics: Syndromic Surveillance for Public Health and Bio-Defense is an in-depth monograph that analyzes and evaluates the outbreak modeling and detection capabilities of existing surveillance systems under a unified framework, and presents the first book-length coverage of the subject from an informatics-driven perspective. Individual chapters consider the state of the art, including the facilitation of data collection, sharing and transmission; a focus on various outbreak detection methods; data visualization and information dissemination issues; and system assessment and other policy issues. Eight chapters then report on several real-world case studies, summarizing and comparing eight syndromic surveillance systems, including those that have been adopted by many public health agencies (e.g., RODS and BioSense). The book concludes with a discussion of critical issues and challenges, with a look to future directions. This book is an excellent source of current information for researchers in public health and IT. Government public health officials and private-sector practitioners in both public health and IT will find the most up-to-date information available, and students from a variety of disciplines, including public health, biostatistics, information systems, computer science, and public administration and policy will get a comprehensive look at the concepts, techniques, and practices of syndromic surveillance.

Introduction The description, planning and governance of nanotechnology development require data on knowledge creation and innovation in various areas of application, how these evolve in time and what is the international context. This book aims to selectively provide such information based on the analysis of databases for science and engineering articles (Thompson Citation Index) and patents (USPTO in the United States, EPO in Europe and JPO in Japan). After a survey of the investigative methods, comparative results per countries, technology fields and research organizations are presented for articles and patents in parts of the interval 1976 to 2006. Interesting features on the evolution of major research themes and connection between research awards and patents have been obtained via longitudinal investigation of the published articles and patent data, as well as connection between NSF funding in nanotechnology and patents awarded to their principal investigators. A web-based system has been developed for accessing and visualizing nanotechnology patents, articles and NSF awards. Scope and Organization The monograph aims to present its chapters in a manner understandable and useful to students, researchers, and nanotechnology professionals. The titles of the eleven chapters are listed below: · Chapter 1. Nanotechnology: An Emerging Field · Chapter 2. Knowledge Mapping: Foundation · Chapter 3. Knowledge Mapping: Analysis Framework · Chapter 4. Mapping Nanotechnology Innovations via USPTO Database: A Longitudinal Study, 1976-2002 · Chapter 5. Federal Funding and Nanotechnology Innovations: NSF Funding and USPTO Patent Analysis, 1991-2002 · Chapter 6.

On September 11, 2001 the World became cognizant of the security challenges it faces on an international scale. With this awareness a commitment has come from the scientific, engineering, and health communities to help the world meet an array of security challenges. From these activities the science of "Intelligence and Security Informatics" is emerging, which will influence a new generation of policy makers, practitioners, researchers, and students. INTELLIGENCE AND SECURITY INFORMATICS FOR INTERNATIONAL SECURITY: Information Sharing and Data Mining presents a systematic national security research framework, and discusses IT technical components, and directions. The book reflects a decade of leading-edge research on intelligence and security informatics from the Artificial Intelligence Laboratory and the NSF COPLINK Center for Homeland Security Information Technology Research, as well as the application in real-world community situations by the center’s director and the book’s author, Dr. Hsinchun Chen.