Understanding user behavior via mobile data analysis

Bulut, Eyuphan; Szymański, Bolesław K.

doi:10.1109/iccw.2015.7247402

Cited by 18 publications

(9 citation statements)

References 15 publications

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“…Vojnovic performed empirical analysis of huge amounts of mobile search logs to discover users' query behavior to optimize the design procedure for mobile services (Vojnovic, 2008). Bulut has considered the contextual data that portrays users' daily life patterns according to the two dimensions of time and location, an acknowledgment that different user groups have different geographical regions and time frames (Bulut and Szymanski, 2015). Yamakami has explored the regularity of mobile website access on the basis of users' click-streams to predict access behavior over the next month (Yamakami, 2006).…”

Section: Literature Reviewmentioning

confidence: 99%

A service-oriented context-awareness reasoning framework and its implementation

Cheng

Yang

Xia

2018

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Purpose This paper aims to propose an extensible, service-oriented framework for context-aware data acquisition, description, interpretation and reasoning, which facilitates the development of mobile applications that provide a context-awareness service. Design/methodology/approach First, the authors propose the context data reasoning framework (CDRFM) for generating service-oriented contextual information. Then they used this framework to composite mobile sensor data into low-level contextual information. Finally, the authors exploited some high-level contextual information that can be inferred from the formatted low-level contextual information using particular inference rules. Findings The authors take “user behavior patterns” as an exemplary context information generation schema in their experimental study. The results reveal that the optimization of service can be guided by the implicit, high-level context information inside user behavior logs. They also prove the validity of the authors’ framework. Research limitations/implications Further research will add more variety of sensor data. Furthermore, to validate the effectiveness of our framework, more reasoning rules need to be performed. Therefore, the authors may implement more algorithms in the framework to acquire more comprehensive context information. Practical implications CDRFM expands the context-awareness framework of previous research and unifies the procedures of acquiring, describing, modeling, reasoning and discovering implicit context information for mobile service providers. Social implications Support the service-oriented context-awareness function in application design and related development in commercial mobile software industry. Originality/value Extant researches on context awareness rarely considered the generation contextual information for service providers. The CDRFM can be used to generate valuable contextual information by implementing more reasoning rules.

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Section: Literature Reviewmentioning

confidence: 99%

A service-oriented context-awareness reasoning framework and its implementation

Cheng

Yang

Xia

2018

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“…Recent technological breakthroughs have extended the mobile phones' features, functions and capabilities, which are now used for more than just communicating or affording applications. Recently, mobile devices are being utilized to know the consuming habits of individuals and communities [1], [2], [3]. Thus, our purpose is to inject this learned cognition into mobile 5G networks to help them grow smarter and be more efficient when faced to the increasing complexity of network management combined with numerous new applications and their heterogeneous needs.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning with partially labeled Data for Indoor Outdoor Detection

Morel¹,

Singh²,

Viho³

2019

2019 16th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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This paper demonstrates the feasibility of an hybrid/semi-supervised classification method for detecting the environment of an active mobile phone, based on both labeled and unlabeled cellular radio data. Precisely, we provide answers to the following question: what is the environment of the mobile user when it is/was experiencing a mobile service/application: indoor or outdoor? Implementing this method within the mobile network is interesting for mobile operators since it has low complexity, is less human intrusive (minimal intervention of mobile users) and more accurate. The semi-supervised classification algorithm learns to identify the environment using large and real collected 3GPP signals measurements. As compared to existing work, in addition to existing parameters used for classification, we propose to also use a radio metric called Timing Advance. It is computed within the mobile network. We empirically validate the innovative semi-supervised algorithm using new real-time radio measurements, with partial ground truth information, gathered daily, weekly, monthly, from indoor and outdoor locations and from multiple typical and diversified environments crossed by mobile users. The study confirms the effectiveness of the proposed scheme compared to the existing supervised classification methods including SVM and Deep Learning.

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“…The communication demand of mobile data traffic may increase further. Furthermore, mobile data traffic has a characteristic that it is biased towards specific times and in certain areas, such as commuting time and at stations, respectively [3]. This characteristic of mobile data traffic decreases the bandwidth utilization efficiency of cellular infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Delay-Tolerance-Based Mobile Data Offloading Using Deep Reinforcement Learning

Mochizuki

Abiko

Saito

et al. 2019

Sensors

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The demand for mobile data communication has been increasing owing to the diversification of its purposes and the increase in the number of mobile devices accessing mobile networks. Users are experiencing a degradation in communication quality due to mobile network congestion. Therefore, improving the bandwidth utilization efficiency of cellular infrastructure is crucial. We previously proposed a mobile data offloading protocol (MDOP) for improving the bandwidth utilization efficiency. Although this method balances a load of evolved node B by taking into consideration the content delay tolerance, accurately balancing the load is challenging. In this paper, we apply deep reinforcement learning to MDOP to solve the temporal locality of a traffic. Moreover, we examine and evaluate the concrete processing while considering a delay tolerance. A comparison of the proposed method and bandwidth utilization efficiency of MDOP showed that the proposed method reduced the network traffic in excess of the control target value by 35% as compared with the MDOP. Furthermore, the proposed method improved the data transmission ratio by the delay tolerance range. Consequently, the proposed method improved the bandwidth utilization efficiency by learning how to provide the bandwidth to the user equipment when MDOP cannot be used to appropriately balance a load.

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Understanding user behavior via mobile data analysis

Cited by 18 publications

References 15 publications

A service-oriented context-awareness reasoning framework and its implementation

A service-oriented context-awareness reasoning framework and its implementation

Machine Learning with partially labeled Data for Indoor Outdoor Detection

Delay-Tolerance-Based Mobile Data Offloading Using Deep Reinforcement Learning

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