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A Big Data Analysis Framework for Model-Based Web User Behavior Analytics

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While basic Web analytics tools are widespread and provide statistics about website navigation, no approaches exist for merging such statistics with information about the Web application structure, content and semantics. Current analytics tools only analyze the user interaction at page level in terms of page views, entry and landing page, page views per visit, and so on. We show the advantages of combining Web application models with runtime navigation logs, at the purpose of deepening the understanding of users behaviour. We propose a model-driven approach that combines user interaction modeling (based on the IFML standard), full code generation of the designed application, user tracking at runtime through logging of runtime component execution and user activities, integration with page content details, generation of integrated schema-less data streams, and application of large-scale analytics and visualization tools for big data, by applying both traditional data visualization techniques and direct representation of statistics on visual models of the Web application.

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A Big Data Analysis Framework for Model-Based Web User Behavior Analytics

  1. 1. Titolo presentazione sottotitolo Milano, XX mese 20XX A Big Data Analysis Framework for Model-Based Web User Behavior Analytics Carlo Bernaschina, Marco Brambila, Andrea Mauri, Eric Umuhoza 6th June 2017
  2. 2. DEIB. Data Science LabICWE2017 – June 6th 2017 Context Web analytics has become the tool of choice to inform both business users and designers. Several tools exist that support analysis of Web server logs and extract information on application usage.
  3. 3. DEIB. Data Science LabICWE2017 – June 6th 2017 Context
  4. 4. DEIB. Data Science LabICWE2017 – June 6th 2017 Context
  5. 5. DEIB. Data Science LabICWE2017 – June 6th 2017 Context Web analytics has become the tool of choice towards taking informed business and interaction design decisions. Several tools exist that support analysis of Web server logs and extract information on application usage. • Usually unaware of the design structure and the actual content managed by the application.
  6. 6. DEIB. Data Science LabICWE2017 – June 6th 2017 Objective Provide valuable insights to designers and decision makers Integration of two approaches: • Web Log Analytics • Model Driven Development
  7. 7. DEIB. Data Science LabICWE2017 – June 6th 2017 Model Driven Development Software development paradigm where the models are the main artefacts of the development process. Main benefits: • Model once, generate for any platform of choice • Validation of the requirements In this specific case: • The model includes the structure of the web application.
  8. 8. DEIB. Data Science LabICWE2017 – June 6th 2017 Interaction Flow Modeling Language Interaction Flow Modeling Language (IFML) is designed for expressing the content, user interaction and control behavior of the front-end of software applications
  9. 9. DEIB. Data Science LabICWE2017 – June 6th 2017 Overview Application Server Data Analyser Storage Analyzer (Spark) Code Generator Webratio IFML Editor (Enriched Analytics Model) Web Server LogRTXLog ModelCode Data Visualization Analysis (b) (a,f) (d) (e) Application DB Web Server (Tomcat) Database (c) Modeling Application Deployment Analysis Visualization Execution
  10. 10. DEIB. Data Science LabICWE2017 – June 6th 2017 Logs Integration (d) (c) (b) Model RTXLog Web Server Log Database RTXLog WebServerLog Model Database EnrichedLog GlobalLog FinalLog RTX.sessionId = WebServer.sessionId AND RTX.timestamp = WebServer.timestamp EnrichedLog.elementId = Model.elementId GlobalLog.tabelName = Database.tableName AND Database.attributeName=“OID” AND GlobalLog.instanceID = Database,value (a) (a) (a) (a) A denormalized view of the logs
  11. 11. DEIB. Data Science LabICWE2017 – June 6th 2017 Navigation Based Analyses Includes information regarding how the users navigate the Web site Examples: • Entrance Rate • Bounce Rate • Page Visit • Residence Time • Link Navigation • …
  12. 12. DEIB. Data Science LabICWE2017 – June 6th 2017 Content Based Analyses Comprehends information regarding the domain entities involved in the user interaction, their types and their semantics. Example (e-commerce website) • Top K Visualized Books • Top K Visualized Authors • Top K Clicked Books • …
  13. 13. DEIB. Data Science LabICWE2017 – June 6th 2017 Structure Based Analyses Comprehends information regarding the kind of widget, visualization, or even navigation pattern used in the user interaction. Example: • Top K elements clicked by users when shown in a map throughout the site • Top K elements clicked when shown in the first three positions of a list • Top K elements clicked when an attribute of type image is shown in the page versus an attribute of type currency. • …
  14. 14. DEIB. Data Science LabICWE2017 – June 6th 2017 Data Visualization Tool Traditional charts like pie charts, bar charts, navigation flow charts and so on..
  15. 15. DEIB. Data Science LabICWE2017 – June 6th 2017 Visual Feedback on Model Editor Three types of visualization: • Color: the analytics is shown through the change of color of the corresponding model element • Label: the analytics is shown with a label on the corresponding model element • Properties: the analytics is shown in a separate property panel
  16. 16. DEIB. Data Science LabICWE2017 – June 6th 2017 Visual Feedback on Model Editor - Color • Residence Time • Page visit • …
  17. 17. DEIB. Data Science LabICWE2017 – June 6th 2017 Visual Feedback on Model Editor - Label • Link Out Ratio • Link In Ratio • Top 1 Clicked Entity • …
  18. 18. DEIB. Data Science LabICWE2017 – June 6th 2017 Visual Feedback on Model Editor - Properties • Top k clicked entities • Top k visualized entities • …
  19. 19. DEIB. Data Science LabICWE2017 – June 6th 2017 Future Work Do not stop at the visualization! • Effects of Model Restructuring on User Behavior • Conversion Rate • Number of user who reach payment Module • Conversion Path: the path (with high contribution) leading to conversion rate • Model Optimization
  20. 20. DEIB. Data Science LabICWE2017 – June 6th 2017 Thanks for your attention Questions? Contact: andrea.mauri@polimi.it Tool: www.ifmledit.org Further Info: http://datascience.deib.polimi.it/bigdata-modeling- weblogs/

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