A pervasive framework for real-time activity patterns of mobile users
Given the rise of ubiquitous computing and communication devices like biosensors, smart watch, and smartphones, real-time online systems can provide users with a wide range of supports including monitoring daily activities and retrieving of personal data. User activity pattern can give an abstraction and summarization about physical behavior for certain group of people. However, one of the biggest challenges in this topic that we are facing today is the big data problem associated with large, complex, and dynamic data. In addition, as the demand for the integration and analysis of dynamic data as well as static historical data from different sources has been growing steadily, smartphones with limited capacity and computing abilities can hardly manage and process such a huge task. To address these above issues, a new framework has to be used to assist in the process, analysis, and integration of big data for a mobile platform. In this paper, I propose a distributed cloud based pervasive framework to help do complicated computing for a mobile platform. The framework has the ability to collect, process, analyze, and integrate different types of data from different sources by using state-of-the-art technologies. The purpose of this framework is to provide an intelligent and efficient approach to analyze and combine new incoming data with historical data to build and refine a solid user activity pattern.