extended-abstract

Device analyzer: large-scale mobile data collection

Authors:

Daniel T. Wagner,

Andrew Rice,

Alastair R. BeresfordAuthors Info & Claims

ACM SIGMETRICS Performance Evaluation Review, Volume 41, Issue 4

Pages 53 - 56

https://doi.org/10.1145/2627534.2627553

Published: 17 April 2014 Publication History

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Abstract

We collected usage information from 12,500 Android devices in the wild over the course of nearly 2 years. Our dataset contains 53 billion data points from 894 models of devices running 687 versions of Android. Processing the collected data presents a number of challenges ranging from scalability to consistency and privacy considerations. We present our system architecture for collection and analysis of this highly-distributed dataset, discuss how our system can reliably collect time-series data in the presence of unreliable timing information, and discuss issues and lessons learned that we believe apply to many other big data collection projects.

References

[1]

D. P. Anderson, J. Cobb, E. Korpela, M. Lebofsky, and D. Werthimer. SETI@home: an experiment in public-resource computing. Comms of the ACM, 45(11), 2002.

Digital Library

Google Scholar

[2]

M. Böhmer, B. Hecht, J. Schöning, A. Krüger, and G. Bauer. Falling Asleep with Angry Birds, Facebook and Kindle A Large Scale Study on Mobile Application Usage. MobileHCI, 2011.

Google Scholar

[3]

L. Carata, R. Sohan, A. Rice, and A. Hopper. IPAPI: Designing an Improved Provenance API. TaPP, 2013.

Digital Library

Google Scholar

[4]

N. Eagle and A. S. Pentland. Reality mining: sensing complex social systems. Personal and Ubiquitous Computing, 10(4), 2005.

Digital Library

Google Scholar

[5]

A. Girardello and F. Michahelles. AppAware: which mobile applications are hot? MobileHCI, 2010.

Digital Library

Google Scholar

[6]

F. Khatib, S. Cooper, M. D. Tyka, K. Xu, I. Makedon, Z. Popovic, D. Baker, and F. Players. Algorithm discovery by protein folding game players. PNAS, 108(47), 2011.

Google Scholar

[7]

M. Langheinrich. Privacy by Design - Principles of Privacy-Aware Ubiquitous Systems. UbiComp, 2001.

Digital Library

Google Scholar

[8]

J. K. Laurila, J. Blom, O. Dousse, D. Gatica-perez, O. Bornet, and M. Miettinen. The Mobile Data Challenge : Big Data for Mobile Computing Research. PerCom, 2012.

Google Scholar

[9]

E. Oliver. The challenges in large-scale smartphone user studies. HotPlanet, 2010.

Digital Library

Google Scholar

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Leierzopf EMayrhofer RRoland MStudier WDean LSeiffert MPutz FBecker LThomas D(2024)A Data-Driven Evaluation of the Current Security State of Android Devices2024 IEEE Conference on Communications and Network Security (CNS)10.1109/CNS62487.2024.10735682(1-9)Online publication date: 30-Sep-2024
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Published In

cover image ACM SIGMETRICS Performance Evaluation Review

ACM SIGMETRICS Performance Evaluation Review Volume 41, Issue 4

March 2014

104 pages

ISSN:0163-5999

DOI:10.1145/2627534

Editor:
Giuliano Casale
Imperial College London, London

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Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 April 2014

Published in SIGMETRICS Volume 41, Issue 4

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Leierzopf EMayrhofer RRoland MStudier WDean LSeiffert MPutz FBecker LThomas D(2024)A Data-Driven Evaluation of the Current Security State of Android Devices2024 IEEE Conference on Communications and Network Security (CNS)10.1109/CNS62487.2024.10735682(1-9)Online publication date: 30-Sep-2024
https://doi.org/10.1109/CNS62487.2024.10735682
Bakar AShariff AHuei CFadilah S(2024)The digital pheromone: Building digital identity of smartphone users based on time-varying multivariatesICT Express10.1016/j.icte.2024.07.00810:5(981-988)Online publication date: Oct-2024
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Device analyzer: a privacy-aware platform to support research on the Android ecosystem

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