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Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior
Experiments on the real mobile phone datasets show that our proposed segmentation technique better captures the user's dominant behavior at various times-of-the-day and days-of-the-week enabling the generation of high confidence temporal rules in order to mine individual mobile phone users' behavior.
SilentPhone: Inferring User Unavailability based Opportune Moments to Minimize Call Interruptions
The increasing popularity of cell phones has made them the most personal and ubiquitous communication devices nowadays.
Mobile Data Science: Towards Understanding Data-Driven Intelligent Mobile Applications
This enables the study on mobile phone data and context-awareness in computing, for the purpose of building data-driven intelligent mobile applications, not only on a single device but also in a distributed environment for the benefit of end users.
Understanding the Role of Data-Centric Social Context in Personalized Mobile Applications
In this paper, we mainly focus on social relational context that represents the interpersonal relationship between individuals, and the role or influence of such context on users' diverse phone call activities in their real world life.
