chapter 1 introduction
Skip this Video
Download Presentation
Chapter 1 Introduction

Loading in 2 Seconds...

play fullscreen
1 / 33

Chapter 1 Introduction - PowerPoint PPT Presentation

  • Uploaded on

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. .

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about ' Chapter 1 Introduction ' - dorjan

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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
  • Document representation
  • Query representation
  • Retrieval model
  • Similarity between document and query
  • Rank the documents
  • Performance evaluation of the retrieval process
keywords versus semantics
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
semantic web1
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
data mining applied to web
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
data mining applied to web1
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
web mining1
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
web usage mining1
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
web usage mining2
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
applications of web usage mining
Applications of Web Usage Mining

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

web content mining1
Web Content Mining
  • Text mining
    • Traditional information retrieval
    • Semantic Web
  • Multimedia
    • Images
    • Audio
    • Video
  • Web crawlers
web structure mining1
Web-Structure Mining
  • 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
  • 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.
build better web sites using intelligent technologies
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
benefits of intelligent web
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