An Efficient Multidimensional Data Model for Web Usage Mining 1st edition by Edmond Wu, Michael Ng, Joshua Huang ISBN 3540213710 9783540213710

An Efficient Multidimensional Data Model for Web Usage Mining 1st edition by Edmond Wu, Michael Ng, Joshua Huang  – Ebook PDF Instant Download/Delivery. 3540213710, 978-3540213710
Full download An Efficient Multidimensional Data Model for Web Usage Mining 1st  Edition after payment

Product details:

ISBN 10:       3540213710
ISBN 13: 978-3540213710
Author:       Edmond H. Wu, Michael K. Ng, Joshua Z. Huang   
 

An Efficient Multidimensional Data Model for Web Usage Mining 1st  Table of contents:

• Introduction

  • 1.1 Overview of Web Usage Mining
  • 1.2 Importance of Multidimensional Models in Web Usage Mining
  • 1.3 Motivation for Developing an Efficient Data Model
  • 1.4 Key Contributions of the Paper
  • 1.5 Structure of the Paper

• Background and Related Work

  • 2.1 Web Usage Mining: Definition and Scope
  • 2.2 Overview of Existing Data Models in Web Mining
  • 2.3 Multidimensional Data Models: Theory and Applications
  • 2.4 Limitations of Current Approaches in Web Usage Mining
  • 2.5 Related Techniques: OLAP, Data Warehousing, and Dimensional Modeling

• Theoretical Foundations

  • 3.1 Basics of Multidimensional Data Models
  • 3.2 Key Concepts: Dimensions, Facts, and Measures
  • 3.3 Data Cubes in Web Usage Mining
  • 3.4 Representing Web Usage Patterns in Multidimensional Space
  • 3.5 Defining Metrics for Web Usage Analysis

• Proposed Multidimensional Data Model for Web Usage Mining

  • 4.1 Design Principles of the Proposed Model
  • 4.2 Data Model Architecture: Layers and Components
  • 4.3 Incorporating Web Usage Data: Logs, Clickstream, and User Behavior
  • 4.4 Defining Dimensions and Measures for Web Usage Analysis
  • 4.5 Data Cube Construction and Querying Methods
  • 4.6 Scalability and Efficiency Considerations in the Data Model

• Data Preprocessing and Transformation

  • 5.1 Data Acquisition from Web Logs and User Interactions
  • 5.2 Cleaning and Filtering Raw Web Usage Data
  • 5.3 Transformation of Raw Data into Multidimensional Format
  • 5.4 Handling Missing Data and Inconsistent Records
  • 5.5 Feature Extraction and Aggregation Techniques

• Efficient Querying and Analysis

  • 6.1 Query Processing Techniques for Web Usage Data
  • 6.2 Efficient Aggregation and Computation in Multidimensional Models
  • 6.3 OLAP (Online Analytical Processing) for Web Usage Mining
  • 6.4 Real-Time Data Analysis and Interactive Querying
  • 6.5 Query Optimization Techniques for Large-Scale Web Data

• Applications of the Multidimensional Data Model

  • 7.1 User Behavior Analysis and Pattern Discovery
  • 7.2 Personalization and Recommendation Systems
  • 7.3 Traffic Analysis and Website Optimization
  • 7.4 Customer Segmentation and Targeting
  • 7.5 E-commerce and Business Intelligence Applications

• Experimental Evaluation

  • 8.1 Experimental Setup and Dataset Description
  • 8.2 Performance Metrics for Evaluating the Model
  • 8.3 Comparison with Existing Web Usage Mining Models
  • 8.4 Case Studies and Application Scenarios
  • 8.5 Empirical Results: Effectiveness and Efficiency of the Model

• Challenges and Open Problems

  • 9.1 Handling High Dimensionality and Sparsity in Web Data
  • 9.2 Real-Time Data Processing and Analysis Challenges
  • 9.3 Scalability of the Proposed Model for Large-Scale Web Logs
  • 9.4 Privacy and Security Issues in Web Usage Mining
  • 9.5 Future Directions and Research Opportunities in Web Mining Models

• Conclusion

  • 10.1 Summary of the Proposed Model and Its Contributions
  • 10.2 Practical Implications for Web Usage Mining
  • 10.3 Final Thoughts on the Future of Multidimensional Data Models
  • 10.4 Closing Remarks and Future Research Directions

People also search for An Efficient Multidimensional Data Model for Web Usage Mining 1st :

an efficient algorithm for exploiting multiple arithmetic units
a multidimensional response to any event
c multidimensional array indexing
efficient parametrization of multi-domain deep neural networks
multidimensional fatigue inventory (mfi-20) scoring