Characterizing User Behavior in Mobile Internet

Abstract

Smart devices bring us the ubiquitous mobile accessing to Internet, making Mobile Internet grow rapidly. Using the mobile traffic data collected at core metropolitan 2G and 3G networks of China over a week, this paper studies the mobile user behavior from three aspects -Data Usage, Mobility Pattern and Application Usage. We classify mobile users into different groups to study the resource consumption in Mobile Internet. We observe that traffic heavy users and high mobility users tend to consume massive data and radio resources simultaneously. Both the data usage and the mobility pattern are closely related to the application access behavior of the users. Users can be clustered through their application usage behavior and application categories can be identified by the ways to attract users. Our analysis provides an comprehensive understanding of user behavior in Mobile Internet, which may be used by network operators to design appropriate mechanisms in resource provision and mobility management for resource consumers based on different categories of applications.

A Survey on Machine Learning-Based Mobile Big Data Analysis: Challenges and Applications

“…For example, we can order taxies, shop, and book hotels using mobile phones. Yang et al [49] provide a comprehensive study on user behaviors in exploiting the mobile Internet. It has been found that many factors, such as data usage and mobility pattern, may impact people's online behavior on mobile devices.…”

“…MBD contains a large variety of information of offline data and online real-time data stream generated from smart mobile terminals, sensors, and services and hastens various applications based on the advancement of data analysis technologies, such as collaborative filtering-based recommendation [46,47], user social behavior characteristics analysis [48][49][50][51], vehicle communications in the Internet of Vehicles (IoV) [52], online smart healthcare [53], and city residents' activity analysis [6]. Although the machine learning-based methods are widely applied in the MBD fields and obtain good performances in real data test, the present methods still need to be further developed.…”

Data Traffic Offload from Mobile to Wi-Fi Networks: Behavioural Patterns of Smartphone Users

“…Similarly, He et al [27] analysed patterns of data traffic generated with various smartphones, in this case with the goal of achieving desired performance levels in mobile networks. Yang et al [28] established a link among the volume of data traffic generated, smartphone-based apps, and user mobility. Binde and Fuksa [29] examined the development of mobile communication networks and information-communication services as factors affecting mobile Internet use in Latvia.…”

A Usage-based Data Extraction Framework for Cloud-based Application - An Human-Computer Interaction Approach

“…The phases that are connected through the straight arrow are sequential, the dashed arrow represents the dependencies or outputs in each phase. Data Understanding refers to understanding what we need to know from the data we intend to extract X X Manual (provided by a company) X (Cito et al, 2015) X X X Cloud Monitoring Tools X (Junco, 2013) X X X X X 3 rd Party software X X (Sarkar et al, 2014) X X Internal Logging System (Al-Bayati et al, 2016) X X Manual (Xu et al, 2016) X X X X X Restful Interfaces (Smit et al, 2013) X X X Existing cloud monitoring tools (Yang et al, 2017) X X X Application Plugins X (Ghezzi et al, 2014) X X X X URL Logging, REST X (Yang et al, 2015) X X Manual (provided by a company) X from the cloud system. The usage data analysis may be useful for the analysis of user behaviour, application performance, software personalisation, recommendation, software development and so on.…”