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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

Chapter 1Introduction

Chapter1 in the textbook

Sections: 1.2, 1.3, 1.4, 1.5, 1.6


The web
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.



New challenges of the web
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


What is wi
What is WI?

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


Web intelligence wi
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


Intelligent web
Intelligent Web

  • Learning new knowledge from the Web

  • Searching for relevant information

  • Personalized web pages

  • Learning about individual users



Information retrieval ir
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


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


Modern information retrieval
Modern Information Retrieval of Ned Fielden, San Francisco State University)

  • Document representation

  • Query representation

  • Retrieval model

  • Similarity between document and query

  • Rank the documents

  • Performance evaluation of the retrieval process


Semantic web
Semantic Web of Ned Fielden, San Francisco State University)


Keywords versus semantics
Keywords versus Semantics of Ned Fielden, San Francisco State University)

  • The traditional IR is limited by keywords

  • Key phrases can be used to introduce a bit of semantics

  • Semantic Web is an emerging area


Semantic web1
Semantic Web of Ned Fielden, San Francisco State University)

  • 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


Web mining
Web Mining of Ned Fielden, San Francisco State University)


Data mining applied to web
Data Mining Applied to Web of Ned Fielden, San Francisco State University)

  • 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


Data mining applied to web1
Data of Ned Fielden, San Francisco State University)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


Web mining1
Web Mining of Ned Fielden, San Francisco State University)

  • 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



Web usage mining
Web Usage Mining Romanko, 2002)


Web usage mining1
Web Usage Mining Romanko, 2002)

  • 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



Web usage mining2
Web Srivastava 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


Applications of web usage mining
Applications of Web Usage Mining Srivastava

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


Web content mining
Web Content Mining Srivastava


Web content mining1
Web Content Mining Srivastava

  • Text mining

    • Traditional information retrieval

    • Semantic Web

  • Multimedia

    • Images

    • Audio

    • Video

  • Web crawlers



Web structure mining
Web Structure Mining Romanko, 2002)


Web structure mining1
Web-Structure Mining Romanko, 2002)

  • Finding the model underlying the link structures of the Web,

  • classify web pages.

  • similarity and relationship between various websites


Web structure mining2
Web Structure Mining Romanko, 2002)

  • 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.


Why web intelligence
Why Web Intelligence? Romanko, 2002)


Build better web sites using intelligent technologies
Build Better Web Sites Using Intelligent Technologies Romanko, 2002)

  • 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


Benefits of intelligent web
Benefits of Intelligent Web Romanko, 2002)

  • 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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