Understanding Mobile App Usage Patterns Using In-App Advertisements View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2013

AUTHORS

Alok Tongaonkar , Shuaifu Dai , Antonio Nucci , Dawn Song

ABSTRACT

Recent years have seen an explosive growth in the number of mobile devices such as smart phones and tablets. This has resulted in a growing need of the operators to understand the usage patterns of the mobile apps used on these devices. Previous studies in this area have relied on volunteers using instrumented devices or using fields in the HTTP traffic such as User-Agent to identify the apps in network traces. However, the results of the former approach are difficult to be extrapolated to real-world scenario while the latter approach is not applicable to platforms like Android where developers generally use generic strings, that can not be used to identify the apps, in the User-Agent field. In this paper, we present a novel way of identifying Android apps in network traces using mobile in-app advertisements. Our preliminary experiments with real world traces show that this technique is promising for large scale mobile app usage pattern studies. We also present an analysis of the official Android market place from an advertising perspective. More... »

PAGES

63-72

Book

TITLE

Passive and Active Measurement

ISBN

978-3-642-36515-7
978-3-642-36516-4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-36516-4_7

DOI

http://dx.doi.org/10.1007/978-3-642-36516-4_7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1018710951


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