29th of June 2016. My presentation done at the 5th IEEE International Conference on Mobile Services (MS 2016).
Accompanying paper: http://www.ivanomalavolta.com/files/papers/MS_2016.pdf
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
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Leveraging Web Analytics for Automatically Generating Mobile Navigation Models [Mobile Services 2016]
1. VRIJE
UNIVERSITEIT
AMSTERDAM
Andrea Salini, Ivano Malavolta, Fabrizio Rossi
i.malavolta@vu.nl
Leveraging Web Analytics for Automatically
Generating Mobile Navigation Models
San Francisco, 29th June 2016
VRIJE
UNIVERSITEIT
AMSTERDAM
2. VRIJE
UNIVERSITEIT
AMSTERDAM
Background
Mobile devices are replacing traditional desktop websites
Ă People is relying more and more on mobile devices
FACT 1
Adam Lella, Andrew Lipsman, Ben Martin.The global mobile report. comsCore white paper, 2015
6. VRIJE
UNIVERSITEIT
AMSTERDAM
Solution
To exploit existing web analytics data for generating
mobile-oriented navigation models
Fully automatic
Catalyst for following design best practices
Generated models are:
1. compliant with the standard IFML language
Ă easily understood by interaction designers
2. tailored to navigational patterns of the users
Ă better control of the design space
SOLUTION
9. VRIJE
UNIVERSITEIT
AMSTERDAM
Web usage mining
âą A node in Gw for each webpage of w
âą An arc in Gw from node u to node v, if there exists a link from page u to page
v that has been navigated at least once by users
âą Each arc arc(u,v) is weighted according to the number of times that the
link(u,v) has been navigated by the users
âą Specialnodes for modelling the arriving of a user (entrance node) and the
leaving of a user (exit node)
Input
Output
The web analytics report
Navigation graph Gw
13. VRIJE
UNIVERSITEIT
AMSTERDAM
Navigation graph refactoring
1. Measuring the centrality of each node in Gw
2. Semantic analysis of page titles
â it helps in distinguishing between generic pages and data-specific pages
Input
Output
Navigation graph Gw
Refactored Gw
Generic page Data-specificpage
17. VRIJE
UNIVERSITEIT
AMSTERDAM
Navigation tree extraction
Input
Output
Refactored Gw
Mobile-oriented navigation tree Tw
Properties of Tw
â contains the most navigated nodes
âą #visualizations * centrality
â reasonable depth Ă users can reach each app screen via fewer taps
â reasonable breadth Ă the informationarchitecture of the app is kept
simple Ă app is more usable
Generationformulated as a variation of the
Steiner Tree Problem with Revenues,
Budget and Hop Constraints
âą It explicitly handles the hop limit (that is, bounds the depth of Tw)
âą Easy to add extra constraints
20. VRIJE
UNIVERSITEIT
AMSTERDAM
Example of IFML model
âą It reflects the navigation patterns of the users of the GSSI website
âą Interaction designers do not start their reasoning process from a blank
canvas
âą It can be refined according to additional business-, project- and
organization-specific requirements
22. VRIJE
UNIVERSITEIT
AMSTERDAM
Conclusions
VRIJE
UNIVERSITEIT
AMSTERDAM
Background
Different structure and information architecture
â causes:display size, different modal context, etc.
Ă a complete redesign may be needed
PROBLEM
!=
In this work we focus on navigation design
VRIJE
UNIVERSITEIT
AMSTERDAM
Intuition
Desktop website
Raw web analytics data
Interaction designers
End users
Navigation model
Mobile app
collects and
aggregates
navigate
analyse and
refine
represents
OUR APPROACH
VRIJE
UNIVERSITEIT
AMSTERDAM
Main stages
âą Only input: web analytics report
âą All stages and intermediate artifacts are masked to interaction designers
VRIJE
UNIVERSITEIT
AMSTERDAM
Implementation
Web analytics
report
Graph operations IFML models
management
WordNET
NetworkX
Prototype available here: http://cs.gssi.infn.it/WANDM