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Chapter 1 Introduction

Chapter 1 Introduction . Chapter1 in the textbook Sections: 1.2, 1.3, 1.4, 1.5, 1.6. The Web. redefines the meanings and processes of business, commerce, marketing, publishing, education, research, government, and development , as well as other aspects of our daily life. .

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Chapter 1 Introduction

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  1. Chapter 1Introduction Chapter1 in the textbook Sections: 1.2, 1.3, 1.4, 1.5, 1.6

  2. The Web redefines the meanings and processes of business, commerce, marketing, publishing, education, research, government, and development, as well as other aspects of our daily life.

  3. What’s the difference?

  4. New challenges of the web • Size • Complexity  we need to modify or enhance existing theories and technologies to deal with the size and complexity of the web

  5. What is WI? “Web Intelligence (WI) exploits Artificial Intelligence (AI) and advanced Information Technology (IT) on the Web and Internet.”

  6. Web Intelligence (WI) • The term WI was conceived in late 1999 • A recent sub discipline in computer science, first WI conference was the Asia-Pacific Conference on WI-2001

  7. Intelligent Web • Learning new knowledge from the Web • Searching for relevant information • Personalized web pages • Learning about individual users

  8. Information Retrieval

  9. Information Retrieval (IR) • As soon as information archives started building, so did information retrieval techniques. • Catalogues, index, table of contents • Computerized information storage and retrieval from 1950 and 60’s • Renewed interest after the advent of the Web

  10. Figure 1.1 Timeline of information and retrieval (Courtesy of Ned Fielden, San Francisco State University)

  11. Modern Information Retrieval • Document representation • Query representation • Retrieval model • Similarity between document and query • Rank the documents • Performance evaluation of the retrieval process

  12. Semantic Web

  13. Keywords versus Semantics • The traditional IR is limited by keywords • Key phrases can be used to introduce a bit of semantics • Semantic Web is an emerging area

  14. Semantic Web • The Semantic Web proposed by Tim Berners-Lee, the developer of the World Wide Web • The Semantic Web is concerned with the representation of data on the World Wide Web. • W3C, researchers and industrial partners

  15. Web Mining

  16. Data Mining Applied to Web • Data mining is the process of discovering knowledge from large amount of data • Used significantly in commercial and scientific applications • Adjustment needs to be made for the Web

  17. Data Mining Applied to Web • Clustering: Finding natural groupings of users or pages • Classification and prediction: Determining the class or behavior of a user or resource • Associations: Determining which URLs tend to be requested together • Sequence Analysis: study the order in which URLs tend to be accessed

  18. Web Mining • Web content mining • Applied to primary data on the Web, text and multimedia documents • Web structure mining • Hyperlink analysis • Web usage mining • Secondary data consisting of user interaction with the Web • User profiles

  19. Figure 1.2 Web mining classifications (Courtesy of O. Romanko, 2002)

  20. Web Usage Mining

  21. Web Usage Mining • Study of data generated by the surfer’s sessions or behaviors • Works with the secondary data from user’s communications with the Web • web logs, proxy-server logs, browser logs • A Web-access log is an inventory of page-reference data • referred to as clickstream data, as each entry corresponds to a mouse click • Cookies

  22. Figure 1.3 High level web usage mining process (Courtesy of Srivastava et al., 2000)

  23. Web Usage Mining • Logs can be observed from two angles: • Server: to advance the design of a website. • Client: assessing a client’s sequence of clicks. • Useful for caching of pages • Efficient loading of Web pages • Helps organizations efficiently market their products on the Web. • Can supply essential information on how to restructure a website

  24. Applications of Web Usage Mining Figure 1.4 Applications of web usage mining (Courtesy of O. Romanko, 2002; Courtesy of Srivastavaet al., 2000)

  25. Web Content Mining

  26. Web Content Mining • Text mining • Traditional information retrieval • Semantic Web • Multimedia • Images • Audio • Video • Web crawlers

  27. Figure 1.5 Architecture of a search engine (Courtesy of O. Romanko, 2002)

  28. Web Structure Mining

  29. Web-Structure Mining • Finding the model underlying the link structures of the Web, • classify web pages. • similarity and relationship between various websites

  30. Web Structure Mining • Algorithms to model web topology • PageRank • HITS • CLEVER • Primarily useful as a technique for computing the rank of every web page • Assumption: if one web page points to another web page, then the former is approving the significance of the latter.

  31. Why Web Intelligence?

  32. Build Better Web Sites Using Intelligent Technologies • Better keyword and key-phrase based search • Multimedia information retrieval using Web content mining • Analyze the shopping trends using data mining • Improve access to website by studying Web usage • Improved structure using Web structure mining

  33. Benefits of Intelligent Web • Matching existing resources to a visitor’s interests • Boost the value of visitors • Enhance the visitor’s experience on the web site • Achieve targeted resource management • Test the significance of content and web site architecture

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