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Web Usage Patterns. Ryan McFadden IST 497E December 5, 2002. Introduction. Web Data Mining Application Areas of Web Data Mining Problems with Web Data Mining Current Research Nielsen//NetRatings Other Issues – Privacy, Security, etc Conclusions . Web Data Mining.

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Web usage patterns l.jpg

Web Usage Patterns

Ryan McFadden

IST 497E

December 5, 2002

Introduction l.jpg

  • Web Data Mining

  • Application Areas of Web Data Mining

  • Problems with Web Data Mining

  • Current Research

  • Nielsen//NetRatings

  • Other Issues – Privacy, Security, etc

  • Conclusions

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Web Data Mining

  • Web Data Mining is the application of data mining techniques to discover and retrieve useful information and patterns from the World Wide Web documents and services.

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What web data is being mined?

  • Content – data from Web documents – text & graphics

  • Structure – data from Web Structure – HTML or XML tags

  • Usage – data from Web log data – IP addresses, date & time access

  • User Profile – data that is user specific – registration and customer profile

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Web Data Mining Process Tasks

  • Resource finding:

    • The task of retrieving intended Web documents

  • Information selection and pre-processing:

    • Automatically selecting and pre-processing specific information from retrieved Web resources

  • Generalization:

    • Automatically discover general patterns at individual Web sites as well as across multiple sites

  • Analysis:

    • Validation and/or interpretation of the mined patterns

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Application Areas for Web Usage Mining

  • Personalization

  • System Improvement

  • Site Modification

  • Business Intelligence

  • Usage Characterization

Personalization l.jpg

  • Personalizing the Web experience for a user is the holy grail of many Web-based applications

  • Dynamic recommendations to a Web user based on a profile in addition to usage behavior

  • The specification to the individual of tailored products, services, information or information relating to products or service

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System Improvement

  • Performance and other service quality attributes are crucial to user satisfaction and high quality performance of a web application is expected

  • Web usage mining of patterns provides a key to understanding Web traffic behavior, which can be used to deal with policies on web caching, network transmission, load balancing, or data distribution

  • Web usage and data mining is also useful for detecting intrusion, fraud, and attempted break-ins to the system

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Site Modification

  • This application of web usage patterns involves the attractiveness of a Web site, in terms of content and structure

  • Web usage patterns or mining can provide detailed feedback on user behavior which can lead the Web site designer to information on which to base redesign decisions

  • This could lead to future applications where the structure and content of a Web site based on usage patterns

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Business Intelligence

  • Information on how customers are using a Web site is critical information for marketers of e-commerce businesses

  • Customer relationship life cycle:

    • Customer attraction

    • Customer retention

    • Cross sales

    • Customer departure

  • Can provide information on products bought and advertisement click-through rates

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Usage Characterization

  • Mining of web usage patterns can help in the study of how browsers are used and the user’s interaction with a browser interface

  • Usage characterization can also look into navigational strategy when browsing a particular site

  • Web usage mining focuses on techniques that could predict user behavior while the user interacts with the Web

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Problems with Web Data Mining

  • The World Wide Web is a huge, diverse and dynamic medium for the dissemination of information – maybe too much information to mine – information overload – a lot of this information is irrelevant and not indexed

  • Other problems with Web Data Mining:

    • Finding relevant information to mine

    • Personalization & mass customization is difficult

    • E-commerce businesses have to know what the customers want

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Current Research

  • WebSIFT example

  • Data Mining for Intelligent Web Caching

  • Areas of Future Research

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WebSIFT Example

  • Web Site Information Filter System (WebSIFT) is a Web usage mining framework, that uses the content and structure information from a Web site, and identifies the interesting results from mining usage data

  • Input of the mining process: server logs (access, referrer, and agent), HTML files, optional data

  • Prototypical Web usage mining system

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Data Mining for Intelligent Web Caching

  • Application based on data warehouse technology that is capable of adapting its behavior based on access patterns of the clients/users

  • Use an algorithm to maximize the hit rate, or percentage of requested Web entities that are retrieved directly in cache, without requesting them back to the origin server

  • This approach enhances least recently used caching with data mining models based on historical data, aimed at increasing the hit rate

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Areas of Future Research

  • Data mining in the following application areas:

    • Electronic Commerce

    • Bioinformatics

    • Computer security

    • Web intelligence

    • Intelligent learning

    • Database systems

    • Finance

    • Marketing

    • Healthcare

    • Telecommunications,

    • And other fields

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  • What are they?

  • What is the purpose?

  • Current NetRatings for home and work

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Nielsen//NetRatings – What are they?

  • This service is provided via a partnership between NetRatings, Nielsen Media Research and ACNielsen

  • The service includes an Internet audience measurement service and they report Internet usage estimates based on a sample of households that have access to the Internet

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Nielsen//NetRatings – What is the purpose?

  • The purpose of the Nielsen//NetRatings service is to provide a source of global information on consumer and business usage of the Internet

  • This information helps companies make business-critical decisions

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Average Web Usage at Home –Month of October 2002, US Data

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Average Web Usage at Work –Month of October 2002, US Data

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September 2002 Global Internet Index Average Usage ( * Home Internet Access)

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Other Issues

  • Privacy

  • Security

  • Intellectual Ownership

  • Visual Data Mining

  • Risk Analysis

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  • Web usage and data mining to find patterns is a growing area with the growth of Web-based applications

  • Application of web usage data can be used to better understand web usage, and apply this specific knowledge to better serve users

  • Web usage patterns and data mining can be the basis for a great deal of future research

References l.jpg

  • Data Mining for Intelligent Web Caching – Francesco Bonchi, Fosca Giannotti, Giuseppe Manco, Mirco Nanni, Dino Pedreschi, Chiara Renso, Salvatore Ruggieri

  • IEEE International Conference on Data Mining -http://www.cs.uvm.edu/~xwu/icdm.html

  • Nielsen//NetRatings – http://www.nielsen-netratings.com

  • Web Usage: Mining: Discovery and Applications of Usage Patterns from Web Data - Jaideep Srivastava, Robert Cooley, Mukund Deshpande, Pang-Ning Tan Dept of CSE – University of Minnesota

  • Web Mining: Pattern Discovery from World Wide Web Transactions -

  • Web Mining Research: A Survey – Raymond Kosala, Hendrik Blockeel Dept of CS Katholieke Universiteit Leuven