TY - GEN
T1 - A pervasive framework for real-time activity patterns of mobile users
AU - Shen, Feichen
N1 - Publisher Copyright:
© 2015 IEEE.
Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.
PY - 2015/6/24
Y1 - 2015/6/24
N2 - 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.
AB - 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.
KW - big data analysis
KW - integration
KW - mobile platform
KW - pervasive framework
KW - user activity pattern
UR - http://www.scopus.com/inward/record.url?scp=84946015674&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84946015674&partnerID=8YFLogxK
U2 - 10.1109/PERCOMW.2015.7134038
DO - 10.1109/PERCOMW.2015.7134038
M3 - Conference contribution
AN - SCOPUS:84946015674
T3 - 2015 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2015
SP - 248
EP - 250
BT - 2015 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th IEEE International Conference on Pervasive Computing and Communication, PerCom Workshops 2015
Y2 - 23 March 2015 through 27 March 2015
ER -