Showing papers by "Eemil Lagerspetz published in 2016"

Where Has My Battery Gone?: A Novel Crowdsourced Solution for Characterizing Energy Consumption

Abstract: The increased complexity of smartphone applications, and the increasing number of system settings affecting these applications, has made it difficult to understand and optimize battery use. This article summarizes the authors' recent work on developing a novel crowdsourced solution for characterizing energy consumption of system settings, subsystem variables, and other context factors. Their technique can simultaneously capture relationships between multiple factors, such as software subsystems and system settings, and provide a unified view of the energy state of a mobile device. They demonstrate the benefits of their approach by analyzing a large-scale crowdsourced dataset. Results of their analysis reveal new insights into battery consumption.

Sovereignty of the Apps: There's more to Relevance than Downloads.

Abstract: The value of mobile apps is traditionally measured by metrics such as the number of downloads, installations, or user ratings. A problem with these measures is that they reflect actual usage at most indirectly. Indeed, analytic companies have suggested that retention rates, i.e., the number of days users continue to interact with an installed app are low. We conduct the first independent and large-scale study of retention rates and usage behavior trends in the wild. We study their impact on a large-scale database of app-usage data from a community of 339,842 users and more than 213,667 apps. Our analysis shows that, on average, applications lose 70% of their users in the first week, while very popular applications (top 100) lose only 45%. It also reveals, however, that many applications have more complex usage behavior patterns due to seasonality, marketing, or other factors. To capture such effects, we develop a novel app-usage behavior trend measure which provides instantaneous information about the "hotness" of an application. We identify typical trends in app popularity and classify applications into archetypes. From these, we can distinguish, for instance, trendsetters from copycat apps. In our results, roughly 40% of all apps never gain more than a handful of users. Less than 0.4% of the remaining 60% are constantly popular, 1% flop after an initial steep rise, and 7% continuously rise in popularity. We conclude by demonstrating that usage behavior trend information can be used to develop better mobile app recommendations. With the proposed usage-based measures (retention and trend), we are able to shift sovereignty in app recommendations back to where it really matters: actual usage statistics, in contrast to download count and user ratings which are prone to manipulation by people.

Too big to mail: On the way to publish large-scale mobile analytics data

Abstract: The Carat project started in 2012 has collected over 1.5 TB of data from over 850,000 mobile users all over the world. The project uses Apache Thrift to transmit data, and Apache Spark to run data analysis tasks, and the gist of the Carat analysis method has been published. While the Carat application code is open source, the data is much harder to share because of its size and privacy concerns. This paper outlines the challenges in sharing such a large-scale dataset with detailed information about smart devices, applications, and their users, and presents some solutions to these challenges.