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Web Usage Patterns

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

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  1. Web Usage Patterns Ryan McFadden IST 497E December 5, 2002

  2. 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

  3. 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.

  4. 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

  5. Web Data Mining Process

  6. 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

  7. Application Areas for Web Usage Mining • Personalization • System Improvement • Site Modification • Business Intelligence • Usage Characterization

  8. Personalization • 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. Current Research • WebSIFT example • Data Mining for Intelligent Web Caching • Areas of Future Research

  15. 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

  16. 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

  17. 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

  18. Nielsen//NetRatings • What are they? • What is the purpose? • Current NetRatings for home and work

  19. 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

  20. 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

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

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

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

  24. Other Issues • Privacy • Security • Intellectual Ownership • Visual Data Mining • Risk Analysis

  25. Conclusions • 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

  26. Any Questions?

  27. References • 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

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