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AGENDA

WEB USAGE MINING FRAMEWORK FOR MINING EVOLVING USER PROFILES IN DYNAMIC WEBSITE DONE BY: AYESHA NUSRATH 07L51A0517 FIRDOUSE AFREEN 07L51A0522. AGENDA. INTRODUCTION SYSTEM ANALYSIS PROJECT DISCRIPTION MODULE DISCRIPTON CONCLUSION FUTURE ENHANCEMENT. INTRODUCTION.

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AGENDA

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  1. WEB USAGE MINING FRAMEWORK FOR MINING EVOLVING USER PROFILES IN DYNAMIC WEBSITEDONE BY: AYESHA NUSRATH 07L51A0517FIRDOUSE AFREEN 07L51A0522

  2. AGENDA INTRODUCTION SYSTEM ANALYSIS PROJECT DISCRIPTION MODULE DISCRIPTON CONCLUSION FUTURE ENHANCEMENT

  3. INTRODUCTION • PURPOSE: we present a complete framework and findings in mining Web usage patterns from Web log files of a real Web site that has all the challenging aspects of real-life. • SCOPE: Web usage mining, includes evolving user Profiles and external data describing ontology of the Web content.

  4. SYSTEM ANALYSIS • EXISTING SYSTEM: The fast pace and large amounts of data available in these online settings have recently made it imperative to use automated data mining or knowledge discovery techniques to discover Web user profiles. • PROPOSED SYSTEM: The different modes of usage or the so-called mass user profiles can be discovered using Web usage mining techniques that can automatically extract frequent access patterns from the history of previous user clickstreams stored in Web log files.

  5. PROJECT DESCRIPTION • HARDWARE REQUIREMENTS: Processor : Pentium III / IV Hard Disk : 40 GB Ram : 256 MB Monitor : 15VGA Colour Mouse : Ball / Optical Keyboard : 102 Keys • SOFTWARE REQUIREMENTS: Operating System : Windows XP professional Front End : Microsoft Visual Studio .Net 2005 Language : Visual C#.Net Back End : SQL Server 2005

  6. MODULE DESCRIPTION • Administration: Handling profile evolution. Integrating semantics in Web usage mining. Profile discovery based on web usage. Pre- process web log file to extract user sessions. • Client: Web access patterns on a Web site are dynamic due not only to the dynamics of Web site content and structure but also to changes in the user’s interests and, thus, their navigation patterns. • Retrieve dynamic info and static info: This description is reminiscent of an information retrieval scenario in the sense that profiles that are retrieved should be as close as possible to the original session data. • Report generation: After filtering out irrelevant entries, the data was segmented into sessions based on the client IP address and a time-out threshold between two consecutive accesses in the same session of 45 minutes.

  7. CONCLUSION & FUTURE WORK CONCLUSION: • A multifaceted user profile summarizes a group of users with similar access activities and consists of their viewed pages, search engine queries and inquiring and inquired companies. FUTURE WORK: • Web pages can be pre-fetched depending on the usage patterns. • Further, the method for analysing sparse data can be used in the study of Web log access.

  8. THANK YOU

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