Cross-community sensing and mining

Bin Guo, Zhiwen Yu, Daqing Zhang, Xingshe Zhou

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

With the developments in information and communications technology (ICT), people are involving in and connecting via various forms of communities in the cyber-physical space, such as online communities, opportunistic (offline) social networks, and location-based social networks. Different communities have distinct features and strengths. With humans playing the bridge role, these communities are implicitly interlinked. In contrast with the existing studies that mostly consider a single community, this article addresses the interaction among distinct communities. In particular, we present an emerging research area-cross-community sensing and mining (CSM), which aims to connect heterogeneous, cross-space communities by revealing the complex linkage and interplay among their properties and identifying human behavior patterns by analyzing the data sensed/collected from multi-community environments. The article describes and discusses the research background, characters, general framework, research challenges, as well as our practice of CSM.

Original languageEnglish
Article number6871682
Pages (from-to)144-152
Number of pages9
JournalIEEE Communications Magazine
Volume52
Issue number8
DOIs
StatePublished - Aug 2014

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