Chapter 1 Introduction
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Transcript of Chapter 1 Introduction
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Chapter 1Introduction
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The Webredefines 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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What’s the difference?
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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
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What is WI?“Web Intelligence (WI) exploits Artificial Intelligence (AI) and advanced Information Technology (IT) on the Web and Internet.”
AI IT WI
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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
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Intelligent Web Learning new knowledge from the Web Searching for relevant information Personalized web pages Learning about individual users
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Information Retrieval
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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
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Figure 1.1 Timeline of information and retrieval (Courtesy of Ned Fielden, San Francisco State University)
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Modern Information RetrievalDocument representationQuery representationRetrieval modelSimilarity between document and
queryRank the documentsPerformance evaluation of the
retrieval process
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Semantic Web
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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
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Semantic WebThe 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
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Web Mining
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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
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Data MiningClustering: Finding natural
groupings of users or pagesClassification and prediction:
Determining the class or behavior of a user or resource
Associations: Determining which URLs tend to be requested together
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Web MiningWeb 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 WebUser profiles
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Figure 1.2 Web mining classifications (Courtesy of O. Romanko, 2002)
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Web Usage Mining
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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
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Figure 1.3 High level web usage mining process (Courtesy of Srivastava et al., 2000)
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Web Usage MiningLogs 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
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Applications of Web Usage Mining
Figure 1.4 Applications of web usage mining (Courtesy of O. Romanko, 2002; Courtesy of Srivastava et al., 2000)
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Web Content Mining
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Web Content Mining Text mining
Traditional information retrieval Semantic Web
Multimedia Images Audio Video
Web crawlers
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Figure 1.5 Architecture of a search engine (Courtesy of O. Romanko, 2002)
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Web Structure Mining
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Web-Structure MiningFinding the model underlying
the link structures of the Web,classify web pages. similarity and relationship
between various websites
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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.
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Why Web Intelligence?
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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
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Benefits of Intelligent WebMatching existing resources to a visitor’s
interestsBoost the value of visitorsEnhance the visitor’s experience on the
web siteAchieve targeted resource managementTest the significance of content and web
site architecture