The Experts below are selected from a list of 8508 Experts worldwide ranked by ideXlab platform
Florian Michahelles - One of the best experts on this subject based on the ideXlab platform.
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where should you focus long tail or superstar an analysis of app adoption on the Android Market
International Conference on Computer Graphics and Interactive Techniques, 2012Co-Authors: Nan Zhong, Florian MichahellesAbstract:This talk presents the examination of the long tail of sales distribution in mobile app Market. With the analysis of a large data set of transactions in Android Market, our research suggests that, rather than being a "Long Tail" Market where unpopular niche products aggregately contribute to substantial portion of sales, the Android Market is more a "Superstar" Market strongly dominated by popular hit products. We also show that though most downloads of paid apps are from cheap apps, but some expensive apps accounts for unproportional large revenue.
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long tail or superstar an analysis of app adoption on the Android Market
2012Co-Authors: Nan Zhong, Florian MichahellesAbstract:Many online Markets are found with a long tail in sales distribution. With the analysis of a large data set of transactions in Android Market, this work first brings the examination of long tail to the mobile application Market. The results suggest that, rather than being a “Long Tail” Market where unpopular niche products aggregately contribute to substantial portion of sales, the Android Market is more a “Superstar” Market strongly dominated by popular hit products. Hit apps are also found to have higher user consumption and satisfaction rate. Besides, we investigate the impact of price and finds that some expensive apps constitute unproportional large sales. Our findings reveal possible different Market structure of mobile app Market and point out challenges to app developers. Author Keywords
Nan Zhong - One of the best experts on this subject based on the ideXlab platform.
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where should you focus long tail or superstar an analysis of app adoption on the Android Market
International Conference on Computer Graphics and Interactive Techniques, 2012Co-Authors: Nan Zhong, Florian MichahellesAbstract:This talk presents the examination of the long tail of sales distribution in mobile app Market. With the analysis of a large data set of transactions in Android Market, our research suggests that, rather than being a "Long Tail" Market where unpopular niche products aggregately contribute to substantial portion of sales, the Android Market is more a "Superstar" Market strongly dominated by popular hit products. We also show that though most downloads of paid apps are from cheap apps, but some expensive apps accounts for unproportional large revenue.
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long tail or superstar an analysis of app adoption on the Android Market
2012Co-Authors: Nan Zhong, Florian MichahellesAbstract:Many online Markets are found with a long tail in sales distribution. With the analysis of a large data set of transactions in Android Market, this work first brings the examination of long tail to the mobile application Market. The results suggest that, rather than being a “Long Tail” Market where unpopular niche products aggregately contribute to substantial portion of sales, the Android Market is more a “Superstar” Market strongly dominated by popular hit products. Hit apps are also found to have higher user consumption and satisfaction rate. Besides, we investigate the impact of price and finds that some expensive apps constitute unproportional large sales. Our findings reveal possible different Market structure of mobile app Market and point out challenges to app developers. Author Keywords
Heng Yin - One of the best experts on this subject based on the ideXlab platform.
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droidscope seamlessly reconstructing the os and dalvik semantic views for dynamic Android malware analysis
USENIX Security Symposium, 2012Co-Authors: Lok Kwong Yan, Heng YinAbstract:The prevalence of mobile platforms, the large Market share of Android, plus the openness of the Android Market makes it a hot target for malware attacks. Once a malware sample has been identified, it is critical to quickly reveal its malicious intent and inner workings. In this paper we present DroidScope, an Android analysis platform that continues the tradition of virtualization-based malware analysis. Unlike current desktop malware analysis platforms, DroidScope reconstructs both the OS-level and Java-level semantics simultaneously and seamlessly. To facilitate custom analysis, DroidScope exports three tiered APIs that mirror the three levels of an Android device: hardware, OS and Dalvik Virtual Machine. On top of DroidScope, we further developed several analysis tools to collect detailed native and Dalvik instruction traces, profile API-level activity, and track information leakage through both the Java and native components using taint analysis. These tools have proven to be effective in analyzing real world malware samples and incur reasonably low performance overheads.
Susanne Boll - One of the best experts on this subject based on the ideXlab platform.
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my app is an experiment experience from user studies in mobile app stores
International Journal of Mobile Human Computer Interaction, 2011Co-Authors: Susanne Boll, Niels Henze, Martin Pielot, Benjamin Poppinga, Torben SchinkeAbstract:Experiments are a cornerstone of HCI research. Mobile distribution channels such as Apple's App Store and Google's Android Market have created the opportunity to bring experiments to the end user. Hardly any experience exists on how to conduct such experiments successfully. This article reports on five experiments that were conducted by publishing Apps in the Android Market. The Apps are freely available and have been installed more than 30,000 times. The outcomes of the experiments range from failure to valuable insights. Based on these outcomes, the authors identified factors that account for the success of experiments using mobile application stores. When generalizing findings it must be considered that smartphone users are a non-representative sample of the world's population. Most participants can be obtained by informing users about the study when the App had been started for the first time. Because Apps are often used for a short time only, data should be collected as early as possible. To collect valuable qualitative feedback other channels than user comments and email have to be used. Finally, the interpretation of collected data has to consider unpredicted usage patterns to provide valid conclusions.
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experiments in the wild public evaluation of off screen visualizations in the Android Market
Nordic Conference on Human-Computer Interaction, 2010Co-Authors: Niels Henze, Benjamin Poppinga, Susanne BollAbstract:Since the introduction of application stores for mobile devices there has been an increasing interest to use this distribution platform to collect user feedback. Mobile application stores can make research prototypes widely available and enable to conduct user studies "in the wild" with participants from all over the world. Previous work published research prototypes to collect qualitative feedback or to collect quantitative attributes of specific prototypes. In this paper we explore how to conduct a study that focuses on a specific task and tries to isolate cause and effect much like controlled experiments in the lab. We compare three visualization techniques for off-screen objects by publishing a game in the Android Market. e.g. we show that the performance of the visualization techniques depends on the number of objects. Using a more realistic task and feedback from a hundred times more participants than previous studies lead to much higher external validity. We conclude that public experiments are a viable tool to complement or replace lab studies.
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push the study to the app store evaluating off screen visualizations for maps in the Android Market
Human-Computer Interaction with Mobile Devices and Services, 2010Co-Authors: Niels Henze, Susanne BollAbstract:The introduction of publicly available application stores for mobile devices enables to publish research prototypes to a wide audience. This distribution channel can be used to conduct studies with participants from all over the world and diverse backgrounds. We report from a study that compares three visualization techniques for off-screen objects on digital maps. Usage data from 362 persons was collected and 105 persons completed an interactive tutorial. Significant differences between the three conditions were found. The results support previous findings but we conjecture that the results are affected by unintended influences.
Guanling Chen - One of the best experts on this subject based on the ideXlab platform.
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appjoy personalized mobile application discovery
International Conference on Mobile Systems Applications and Services, 2011Co-Authors: Bo Yan, Guanling ChenAbstract:The explosive growth of the mobile application Market has made it a significant challenge for the users to find interesting applications in crowded App Stores. To alleviate this problem, existing industry solutions often use the users' application download history and possibly their ratings to recommend applications that might interest them, much like Amazon's book recommendations. However, the user downloading an application is a weak indicator of whether the user likes that application, particularly if the application is free and the user just wants to try it out. Using application ratings, on the other hand, suffers from tedious manual input and potential data sparsity problems. In this paper, we present the AppJoy system that makes personalized application recommendations by analyzing how the user actually uses her installed applications. Based on all participants' application usage records, AppJoy employs an item-based collaborative filtering algorithm for individualized recommendations. We discuss AppJoy's design and implementation, and the evaluation shows that it consumes little resource on the off-the-shelf Google Android phones. AppJoy has been available in the Android Market and used by more than 4600 users. The AppJoy's prediction algorithm provided reasonably accurate usage estimate of the recommended applications after they were installed. We also found AppJoy to be effective as the users interacted with recommended applications longer than other applications.