research-article

A Measurement-based Study on Application Popularity in Android and iOS App Stores

Authors:

Wei Liu,

Ge Zhang,

Jun Chen,

Yuze Zou,

Wenchao DingAuthors Info & Claims

Mobidata '15: Proceedings of the 2015 Workshop on Mobile Big Data

Pages 13 - 18

https://doi.org/10.1145/2757384.2757392

Published: 21 June 2015 Publication History

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Abstract

Mobile application stores (appstores) are emerging digital distribution platforms with explosive growth. Although there have been some observations on the mobile application (app) popularity in Android appstores, there is no report on the app popularity in iOS appstores. What's more, the details about user downloads and app popularity, such as the composition of downloads traffic and the migration of user interests, are untouched yet. In this paper, we unreel these issues based on five-month measurements of four third-party appstores (two for Android and two for iOS respectively).

Our main results include: 1) The app popularity distributions of third-party Android appstores are different from those of iOS third-party appstores. There is an exponential cut-off observed besides the Zipf-like distribution in the app popularity distribution of Android appstores. 2) In both Android and iOS families of appstores, the major part of downloads traffic is contributed by the large-size apps, counting 80% or more in the volume of total downloads traffic. 3) There is less rank variance of the most popular apps in the iOS appstores than those in the Android appstores. About 52% of the top 100 iOS apps observed in one month are still in the rank of top 100 in the following four months.

References

[1]

Portio Research. Mobile Applications Futures 2013 - 2017. http://www.portioresearch.com/en/major-reports/current-portfolio/mobile-applications-futures-2013-2017.aspx.

Google Scholar

[2]

CNNIC (China Internet Network Information Center). The 34th Statistical Report on Internet Development in China (Jul. 2014). http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/201407/P020140721507223212132.pdf.

Google Scholar

[3]

H. Abrahamsson and M. Nordmark. Program popularity and viewer behaviour in a large TV- on-demand system. In Proc. ACM IMC'12, pages 199--210, 2012.

Digital Library

Google Scholar

[4]

J. Lin, Z. Li, G. Xie, Y. Sun, K. Salamatian, and W. Wang. Mobile video popularity distributions and the potential of peer-assisted video delivery. In IEEE Comm. Magazine, 51(11): 120--126, 2013.

Crossref

Google Scholar

[5]

M. Hefeeda and O. Saleh. Traffic modeling and propor tional partial caching for peer-to-peer systems. IEEE/ ACM Trans. on Networking, 16(6): 1447--1460, 2008.

Digital Library

Google Scholar

[6]

N. Zhong and F. Michahelles. Google play is not a long tail market: an empirical analysis of app adoption on the Google play app market. In Proc. ACM SAC'13, pages 499--504, 2013.

Digital Library

Google Scholar

[7]

T. Petsas, A. Papadogiannakis, M. Polychronakis, E. P. Markatos, and T. Karagiannis. Rise of the planet of the apps: a systematic study of the mobile app ecosystem. In Proc. ACM IMC'13, pages 277--290, 2013.

Digital Library

Google Scholar

[8]

Mashable. Google Play Hits 1 Million Apps. http://ma shable.com/2013/07/24/google-play-1-million/.

Google Scholar

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Steinböck MBleier JRainer MUrban TUtz CLindorfer MSpinellis DConstantinou EBacchelli A(2024)Comparing Apples to Androids: Discovery, Retrieval, and Matching of iOS and Android Apps for Cross-Platform AnalysesProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644896(348-360)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644896
Hadi MFard F(2023)Evaluating pre-trained models for user feedback analysis in software engineering: a study on classification of app-reviewsEmpirical Software Engineering10.1007/s10664-023-10314-x28:4Online publication date: 23-May-2023
https://dl.acm.org/doi/10.1007/s10664-023-10314-x
Bijlani ARamachandran UCampbell R(2021)Where did my 256 GB go? A Measurement Analysis of Storage Consumption on Smart Mobile DevicesProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/34600955:2(1-28)Online publication date: 4-Jun-2021
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Index Terms

A Measurement-based Study on Application Popularity in Android and iOS App Stores
1. General and reference
  1. Cross-computing tools and techniques
    1. Measurement
    2. Metrics

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Published In

Mobidata '15: Proceedings of the 2015 Workshop on Mobile Big Data

June 2015

84 pages

ISBN:9781450335249

DOI:10.1145/2757384

Program Chairs:
Qun Li
The College of William & Mary, USA
,
Dong Xuan
Ohio State University, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 21 June 2015

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Nature Science Foundation of China(NSFC)

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MobiHoc'15

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SIGMOBILE

MobiHoc'15: The Sixteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing

June 21, 2015

Hangzhou, China

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Steinböck MBleier JRainer MUrban TUtz CLindorfer MSpinellis DConstantinou EBacchelli A(2024)Comparing Apples to Androids: Discovery, Retrieval, and Matching of iOS and Android Apps for Cross-Platform AnalysesProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644896(348-360)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644896
Hadi MFard F(2023)Evaluating pre-trained models for user feedback analysis in software engineering: a study on classification of app-reviewsEmpirical Software Engineering10.1007/s10664-023-10314-x28:4Online publication date: 23-May-2023
https://dl.acm.org/doi/10.1007/s10664-023-10314-x
Bijlani ARamachandran UCampbell R(2021)Where did my 256 GB go? A Measurement Analysis of Storage Consumption on Smart Mobile DevicesProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/34600955:2(1-28)Online publication date: 4-Jun-2021
https://dl.acm.org/doi/10.1145/3460095
Alseadoon IAhmad AAlkhalil ASultan K(2021)Migration of existing software systems to mobile computing platforms: a systematic mapping studyFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-019-8166-515:2Online publication date: 1-Apr-2021
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Wang XWang CChen XFu XHan JWang X(2020)Measurement and analysis on large-scale offline mobile app dissemination over device-to-device sharing in mobile social networksWorld Wide Web10.1007/s11280-020-00807-w23:4(2363-2389)Online publication date: 27-Mar-2020
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Pasteris SWang SHerbster MHe T(2019)Service Placement with Provable Guarantees in Heterogeneous Edge Computing SystemsIEEE INFOCOM 2019 - IEEE Conference on Computer Communications10.1109/INFOCOM.2019.8737449(514-522)Online publication date: Apr-2019
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Wilson ACrabb M(2018)W3C Accessibility Guidelines for Mobile GamesThe Computer Games Journal10.1007/s40869-018-0058-77:2(49-61)Online publication date: 2-May-2018
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Petsas TPapadogiannakis APolychronakis MMarkatos EKaragiannis T(2017)Measurement, Modeling, and Analysis of the Mobile App EcosystemACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/29934192:2(1-33)Online publication date: 6-Mar-2017
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Exploiting Usage to Predict Instantaneous App Popularity: Trend Filters and Retention Rates

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