Self-similarity in World Wide Web traffic: evidence and possible causes
IEEE/ACM Transactions on Networking (TON)
Generating representative Web workloads for network and server performance evaluation
SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Measurement, modeling, and analysis of a peer-to-peer file-sharing workload
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
PlanetLab: an overlay testbed for broad-coverage services
ACM SIGCOMM Computer Communication Review
Impact of search engines on page popularity
Proceedings of the 13th international conference on World Wide Web
Analyzing client interactivity in streaming media
Proceedings of the 13th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Proceedings of the 8th international conference on Mobile systems, applications, and services
A first look at traffic on smartphones
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
A first look at mobile hand-held device traffic
PAM'10 Proceedings of the 11th international conference on Passive and active measurement
TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones
OSDI'10 Proceedings of the 9th USENIX conference on Operating systems design and implementation
A study of android application security
SEC'11 Proceedings of the 20th USENIX conference on Security
Android permissions demystified
Proceedings of the 18th ACM conference on Computer and communications security
Identifying diverse usage behaviors of smartphone apps
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
Unsafe exposure analysis of mobile in-app advertisements
Proceedings of the fifth ACM conference on Security and Privacy in Wireless and Mobile Networks
Dissecting Android Malware: Characterization and Evolution
SP '12 Proceedings of the 2012 IEEE Symposium on Security and Privacy
ProfileDroid: multi-layer profiling of android applications
Proceedings of the 18th annual international conference on Mobile computing and networking
Breaking for commercials: characterizing mobile advertising
Proceedings of the 2012 ACM conference on Internet measurement conference
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Understanding mobile app usage patterns using in-app advertisements
PAM'13 Proceedings of the 14th international conference on Passive and Active Measurement
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Mobile applications (apps) have been gaining rising popularity due to the advances in mobile technologies and the large increase in the number of mobile users. Consequently, several app distribution platforms, which provide a new way for developing, downloading, and updating software applications in modern mobile devices, have recently emerged. To better understand the download patterns, popularity trends, and development strategies in this rapidly evolving mobile app ecosystem, we systematically monitored and analyzed four popular third-party Android app marketplaces. Our study focuses on measuring, analyzing, and modeling the app popularity distribution, and explores how pricing and revenue strategies affect app popularity and developers' income. Our results indicate that unlike web and peer-to-peer file sharing workloads, the app popularity distribution deviates from commonly observed Zipf-like models. We verify that these deviations can be mainly attributed to a new download pattern, to which we refer as the clustering effect. We validate the existence of this effect by revealing a strong temporal affinity of user downloads to app categories. Based on these observations, we propose a new formal clustering model for the distribution of app downloads, and demonstrate that it closely fits measured data. Moreover, we observe that paid apps follow a different popularity distribution than free apps, and show how free apps with an ad-based revenue strategy may result in higher financial benefits than paid apps. We believe that this study can be useful to appstore designers for improving content delivery and recommendation systems, as well as to app developers for selecting proper pricing policies to increase their income.