Applications

Xiang Cheng; Luoyang Fang; Liuqing Yang; Shuguang Cui

doi:10.1007/978-3-319-96116-3_5

Applications

Xiang Cheng, Luoyang Fang, Liuqing Yang, Shuguang Cui

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Analytics and mining over mobile big data enriched with time and location information will provide great opportunities for new services. The potential applications driven by such mobile user data could be roughly divided into two categories. One is mining on the individual user data to provide personalized services (e.g., context-aware sensing, point of interests, activity recognition, etc.). The other is mining on the aggregation of mobile user data to learn and analyze the pattern of human activities, which aims to understand human behaviors in order to help public service planning and city management (e.g., social response monitoring in lieu of social events or disasters, anomaly detection, traffic flow pattern learning, city zone characterization, etc.).

Original language	English (US)
Title of host publication	Wireless Networks(United Kingdom)
Publisher	Springer Science and Business Media B.V.
Pages	51-70
Number of pages	20
DOIs	https://doi.org/10.1007/978-3-319-96116-3_5
State	Published - 2018
Externally published	Yes

Publication series

Name	Wireless Networks(United Kingdom)
ISSN (Print)	2366-1186
ISSN (Electronic)	2366-1445

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Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.

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10.1007/978-3-319-96116-3_5

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AU - Cheng, Xiang

AU - Fang, Luoyang

AU - Yang, Liuqing

AU - Cui, Shuguang

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AB - Analytics and mining over mobile big data enriched with time and location information will provide great opportunities for new services. The potential applications driven by such mobile user data could be roughly divided into two categories. One is mining on the individual user data to provide personalized services (e.g., context-aware sensing, point of interests, activity recognition, etc.). The other is mining on the aggregation of mobile user data to learn and analyze the pattern of human activities, which aims to understand human behaviors in order to help public service planning and city management (e.g., social response monitoring in lieu of social events or disasters, anomaly detection, traffic flow pattern learning, city zone characterization, etc.).

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Applications

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