Web Information Systems And Mining
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Mining the World Wide Web
Author: George Chang
language: en
Publisher: Springer Science & Business Media
Release Date: 2001-07-31
Mining the World Wide Web: An Information Search Approach explores the concepts and techniques of Web mining, a promising and rapidly growing field of computer science research. Web mining is a multidisciplinary field, drawing on such areas as artificial intelligence, databases, data mining, data warehousing, data visualization, information retrieval, machine learning, markup languages, pattern recognition, statistics, and Web technology. Mining the World Wide Web presents the Web mining material from an information search perspective, focusing on issues relating to the efficiency, feasibility, scalability and usability of searching techniques for Web mining. Mining the World Wide Web is designed for researchers and developers of Web information systems and also serves as an excellent supplemental reference to advanced level courses in data mining, databases and information retrieval.
Web Information Systems and Mining
The volume LNCS 7529 constitutes the refereed proceedings of the International Conference on Web Information Systems and Mining, WISM 2012, held in Chengdu, China, in October 2012. The 87 revised full papers presented were carefully reviewed and selected from 418 submissions. The papers are organized in topical sections on applications of Web information systems; applications of Web mining; e-government and e-commerce; information security; intelligent networked systems; management information systems; mobile computing; semantic Web and ontologies; Web information extraction; Web intelligence; Web interfaces and applications; and XML and semi-structured data.
Dark Web
Author: Hsinchun Chen
language: en
Publisher: Springer Science & Business Media
Release Date: 2011-12-16
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.