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.

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. 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. DEIB. Data Science LabICWE2017 – June 6th 2017
Context

4. DEIB. Data Science LabICWE2017 – June 6th 2017
Context

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. 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. 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. 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. 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. 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. 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. 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. 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. DEIB. Data Science LabICWE2017 – June 6th 2017
Data Visualization Tool
Traditional charts like pie charts, bar charts, navigation flow
charts and so on..

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. DEIB. Data Science LabICWE2017 – June 6th 2017
Visual Feedback on Model Editor - Color
• Residence Time
• Page visit
• …

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. DEIB. Data Science LabICWE2017 – June 6th 2017
Visual Feedback on Model Editor - Properties
• Top k clicked
entities
• Top k visualized
entities
• …

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. 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/