The document describes the PRADA approach for prioritizing Android devices for apps by mining large-scale usage data. PRADA collects usage data from Wandoujia, clusters similar apps and device models, then applies collaborative filtering to accurately predict major device models for a new app. It was evaluated on two app categories and achieved satisfactory accuracy in device model hit rates and time share coverage. Future work could explore the impact of localization and different device model clustering granularities.

1. PRADA: Prioritizing Android
Devices for Apps by Mining
Large-Scale Usage Data
Presented by: Akshay Mittal
Course: CS 5393
Professor: Dr. Guowei Yang

2. Authors
• Lu, Xuan;
• Liu, Xuanzhe;
• Li, Huoran;
• Xie, Tao; Mei,
• Qiaozhu;
• Hao, Dan;
• Huang, Gang;
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3. Overview
• Motivation
• Approach
• Evaluation
• Strength
• Weakness
• Conclusion
• Q&A
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4. Overview of PRADA Approach
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5. Motivation
• Android Fragmentation that is,
concern over the alarming
number of different
available Android operating
system (OS) versions in the
market.
• to explore whether we can
make accurate estimates for
a new app.
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7. Approach
Wandoujia
•Native Management
Tool
In Nutshell
•Usage Data Collection
•Similar App Selection
•Device Model Clustering
•Collaborative Filtering
• Operational profiling-how to
increase productivity and
reliability.
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8. Wandoujia
Features
Network Activity
statistics
Permission
Monitoring
Content
recommendation
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9. Approach (cont..)
Effectiveness Metrics
Device
Model Hit
Average
Precision
Usage Data
Coverage
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10. Time-Share Driven Prioritization
• Browsing time on app • Collaborative Filtering by Time
Share
It Uses, Leave-One-Out Cross-
Validation (LOOCV)
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11. Algorithm-I
Device model hit (DH), time
share coverage (T C) and
average precision (AP) against
top N device models with K
apps in the same category
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12. Evaluation
1. Device Model Distribution
• Explains, RQ1: How many
device models account for
the majority of the browsing
time?
• Uses, Pareto Principle
2. Predicting Top Device Model
• Explains, RQ2: How effectively
can PRADA identify major
device models for a new app
given that developers have
no knowledge about this
app’s actual usage?
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13. Device Model Distribution Statistics
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Number of device models and users that use top 100 apps
from each of the two categories.

14. Results from Predicting Top Device Models
Using collaborative filtering algorithm on Game and Media Apps
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Top 10 device models with the most time share for two apps (Temple Run 2 and Xunlei Movie),
and the selected device models by AppBrain, Wandoujia, and PRADA.

15. Comparison of Device Model Hit, Time Share Coverage, and AP by using market share
and PRADA to recommend top 10 device models for Game apps
Results of Device Model Hit, Time Share Coverage, and AP of top 10 device
models that are predicted by PRADA for 100 apps in each category, i.e., N =10
and K=100.
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16. Strength of Prada
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 Mining from large scale of data
 Leverage usage data
 Satisfactory accuracy
 Operational Profiling

17. Weakness
• Restricted on only two network categories
• Need access to existing data usage
• Not accurate for offline apps
• Relies on accuracy of Wandoujia
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18. Related work
• A framework for detecting similar mobile applications by online
kernel learning.
• Rescaling reliability bounds for a new operational profile
• Mining large-scale smartphone data for personality studies.
• Prioritizing the devices to test your app on: A case study of
Android game apps.
• Understanding Android fragmentation with topic analysis of
vendor-specific bugs.
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19. Conclusion
PRADA includes a collaborative filtering
technique to accurately predict major
device models for a new app, given the
usage data from existing apps with similar
functionalities.
Future work a) impact of localization on
device model prioritization.
b) how to cluster device models at different
granularities.
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20. Questions for Deeper Analysis
• How the system can be efficient without
the time-share-based technique not
included in Wandoujia dataset?
• Why only browsing time is main parameter
in analysis?
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21. Any Questions?
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