On the Understanding of Interdependency of Mobile App Usage

Huang, Jiaxin; Xu, Fengli; Lin, Yujun; Li, Yong

doi:10.1109/mass.2017.89

Cited by 10 publications

(5 citation statements)

References 8 publications

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“…The data collected generally cover most mobile users in an entire city [31] or a country [32]. Due to the large volume of network traffic data, network operators have to use sample strategies, collecting network traffic records at regular intervals like every hour [33] or every several minutes [34]. The datasets collected by network operators typically include location data for app usage records.…”

Section: A Data Collection Methodsmentioning

confidence: 99%

“…By analyzing a dataset spanning seven years, Fan et al [97] looked into how usage context changes over time. Huang et al [34] and Liu et al [98] identified frequent cooccurrence app sets. They discovered that e-commerce and online payment apps, such as Taobao and Alipay, are frequently used together to complete the task of online shopping.…”

Section: B App Usage Pattern Discoverymentioning

confidence: 99%

“…Most existing studies are based on biased populations. The users involved in [34], [43], [69], [78], [169], [187] are all Chinese. The participants in [39], [101], [216] all live in the Lake Geneva region in Switzerland.…”

Section: A Challenge In Data 1) Data Collectionmentioning

confidence: 99%

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Smartphone App Usage Analysis: Datasets, Methods, and Applications

Tong

Wang

et al. 2022

IEEE Commun. Surv. Tutorials

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As smartphones have become indispensable personal devices, the number of smartphone users has increased dramatically over the last decade. These personal devices, which are supported by a variety of smartphone apps, allow people to access Internet services in a convenient and ubiquitous manner. App developers and service providers can collect fine-grained app usage traces, revealing connections between users, apps, and smartphones. We present a comprehensive review of the most recent research on smartphone app usage analysis in this survey. Our survey summarizes advanced technologies and key patterns in smartphone app usage behaviors, all of which have significant implications for all relevant stakeholders, including academia and industry. We begin by describing four data collection methods: surveys, monitoring apps, network operators, and app stores, as well as nine publicly available app usage datasets. We then systematically summarize the related studies of app usage analysis in three domains: app domain, user domain, and smartphone domain. We make a detailed taxonomy of the problem studied, the datasets used, the methods used, and the significant results obtained in each domain. Finally, we discuss future directions in this exciting field by highlighting research challenges.

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Section: A Data Collection Methodsmentioning

confidence: 99%

Section: B App Usage Pattern Discoverymentioning

confidence: 99%

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Smartphone App Usage Analysis: Datasets, Methods, and Applications

Tong

Wang

et al. 2022

IEEE Commun. Surv. Tutorials

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“…Due to the random arrivals, the energy consumption has more variance with a larger arrival rate. As application usage depends on a variety of contextual cues such as time and location [29], it is highly possible that there is few application usage. Fig.…”

Section: B Simulation Evaluationmentioning

confidence: 99%

Energy Minimization for Federated Asynchronous Learning on Battery-Powered Mobile Devices via Application Co-running

Hu¹,

Wu²

2022

Preprint

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Energy is an essential, but often forgotten aspect in large-scale federated systems. As most of the research focuses on tackling computational and statistical heterogeneity from the machine learning algorithms, the impact on the mobile system still remains unclear. In this paper, we design and implement an online optimization framework by connecting asynchronous execution of federated training with application co-running to minimize energy consumption on battery-powered mobile devices. From a series of experiments, we find that corunning the training process in the background with foreground applications gives the system a deep energy discount with negligible performance slowdown. Based on these results, we first study an offline problem assuming all the future occurrences of applications are available, and propose a dynamic programmingbased algorithm. Then we propose an online algorithm using the Lyapunov framework to explore the solution space via the energy-staleness trade-off. The extensive experiments demonstrate that the online optimization framework can save over 60% energy with 3 times faster convergence speed compared to the previous schemes.

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“…Users' true actions in apps can be used to understand or infer users' behaviour. Users' behaviour can be extracted from mobile Web logs using various data mining techniques, such as association rule mining [3,12]. However, the mining result might be too coarse if we use a single log entry as a viewpoint, while a single user session might be too broad to give a fine-grained knowledge.…”

Section: Introductionmentioning

confidence: 99%

Identifying In-App User Actions from Mobile Web Logs

Priyogi

Sanderson

Salim

et al. 2018

Advances in Knowledge Discovery and Data Mining

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We address the problem of identifying in-app user actions from Web access logs when the content of those logs is both encrypted (through HTTPS) and also contains automated Web accesses. We find that the distribution of time gaps between HTTPS accesses can distinguish user actions from automated Web accesses generated by the apps, and we determine that it is reasonable to identify meaningful user actions within mobile Web logs by modelling this temporal feature. A real-world experiment is conducted with multiple mobile devices running some popular apps, and the results show that the proposed clusteringbased method achieves good accuracy in identifying user actions, and outperforms the state-of-the-art baseline by 17.84%.

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On the Understanding of Interdependency of Mobile App Usage

Cited by 10 publications

References 8 publications

Smartphone App Usage Analysis: Datasets, Methods, and Applications

Smartphone App Usage Analysis: Datasets, Methods, and Applications

Energy Minimization for Federated Asynchronous Learning on Battery-Powered Mobile Devices via Application Co-running

Identifying In-App User Actions from Mobile Web Logs

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