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Web Communities

Autor Yanchun Zhang, Jeffrey Xu Yu, Jingyu Hou
en Limba Engleză Hardback – 8 dec 2005
Due to the lack of a uniform schema for Web documents and the sheer amount and dynamics of Web data, both the effectiveness and the efficiency of information management and retrieval of Web data is often unsatisfactory when using conventional data management techniques.
Web community, defined as a set of Web-based documents with its own logical structure, is a flexible and efficient approach to support information retrieval and to implement various applications. Zhang and his co-authors explain how to construct and analyse Web communities based on information like Web document contents, hyperlinks, or user access logs. Their approaches combine results from Web search algorithms, Web clustering methods, and Web usage mining. They also detail the necessary preliminaries needed to understand the algorithms presented, and they discuss several successful existing applications.
Researchers and students in information retrieval and Web search find in this all the necessary basics and methods to create and understand Web communities. Professionals developing Web applications will additionally benefit from the samples presented for their own designs and implementations.
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Specificații

ISBN-13: 9783540277378
ISBN-10: 3540277374
Pagini: 200
Ilustrații: XI, 187 p.
Dimensiuni: 160 x 241 x 17 mm
Greutate: 0.42 kg
Ediția:2006
Editura: Springer
Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

Preliminaries.- HITS and Related Algorithms.- PageRank Related Algorithms.- Affinity and Co-Citation Analysis Approaches.- Building a Web Community.- Web Community Related Techniques.- Conclusions.

Recenzii

The book can be used by applied mathematicians, search industry professionals, and anyone who wants to learn more about how search engines work. I recommend it for any course on Web information retrieval. I firmly believe that this book and the book by Langville and Meyer are the top two books about the algorithmic aspects of modern search engines. (Yannis Manolopoulos, Aristotle University, Thessaloniki, Greece in ACM REVIEWS)

Notă biografică

Hua Wang Hua is now a full time Professor in ISILC at Victoria University. Hua has more than ten years teaching and working experience in Applied Informatics at both enterprise and university. He has expertise in Big Data, Health Informatics, Cloud Computing, Cyber Security, and Artificial Intelligence. As a Chief Investigator, Hua has successfully received six in total Australian Research Council (ARC) grants including four Discovery grants and two Linkage grants with 2.3M since 2006. He is also a successor for international grants such as two Japan - Australian grants, one German - Australian grant, one Norway Government grant and grants from Hong Kong Chinese University, Hong Kong City University. Hua has published 232 refereed scholar papers including 46 Q1 (ERA2015 ranked) Journal papers in cyber security, Artificial Intelligence, data mining, database, privacy preserving and Web services. Representative publications are onACM Transactions on Internet Technology, ACM Transactions on Information Systems, ACM Transactions on Knowledge Discovery from Data, IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Automation Science and Engineering and World Wide Web; and Proceedings of ACL, AAAI, CIKM, ICDE, ICDM and PAKDD (https://scholar.google.com.au/citations?user=U7B_FmYAAAAJ&hl=en). Hua's h-index is 34 based on Google Scholar website. As a principal supervisor, he currently has 14 PhD students at VU and has successfully supervised 7 PhD students. Yanchun Zhang Yanchun Zhang is a Professor of Computer Science at Victoria University, Australia. Dr. Zhang obtained a PhD degree in Computer Science from The University of Queensland in 1991. His research interests include databases, data mining, web services and e-health. He has published over 300 research papers in international journalsand conference proceedings including ACM Transactions on Computer and Human Interaction (TOCHI), IEEE Transactions on Knowledge and Data Engineering (TKDE), VLDBJ, SIGMOD and ICDE conferences, and a dozen of books and journal special issues in the related areas. Dr. Zhang is a founding editor and editor-in-chief of World Wide Web Journal (Springer) and Health Information Science and Systems Journal (Springer), and also the founding editor of Web Information Systems Engineering Book Series and Health Information Science Book Series. He is Chairman of International Web information Systems Engineering Society (WISE). His research has been supported by various competitive funding, including 7 prestigious ARC Discovery and ARC Linkage Projects. In addition to high quality research output/publications (in Pattern Recognition, IEEE TKDE, Information Systems, JCSS, etc), Prof Zhang has made strong impact through his leadership in applied research and collaborations with government and industry organizations such as World Health Organization and Tasmanian Department of Health and Human Services, Australian e-Health Research Centre. Their multidisciplinary research into e-health in collaboration with World Health Organization and Tasmanian Department of Health and Human Services have produced software systems and mapping tools to help the government/industry organizations establish health needs, allowing it to base policy on firm evidence. Representative work on gait classification in children with cerebral palsy is published in Pattern Recognition (2009, ERA A*). Recent collaborations with World Health Organisation (WHO) and Tasmania Department of Health and Human Services (DHHS) have produced several software systems adopted by WHO and Tasmania DHHS. Jinli Cao Dr. Cao has been a full-time senior lecturer at department of computer science and Information technology, La Trobe University, Australia. Dr Cao is an active researcher in the evolutionary fields of computer science, data engineering and

Caracteristici

Systematically presents, describes and discusses representative algorithms for Web community construction and analysis Highlights various important applications of Web community Summarizes the main work in this area, and identifies several open research directions that readers can pursue in the future Includes supplementary material: sn.pub/extras