基于端和云的大规模上下文管理框架的研究与实现  

Research and Implementation of Large-scale Context Management Framework Based on Terminal and Cloud

作　　者：史殿习[1] 吴振东[1] 丁博[1] 

机构地区：[1]国防科学技术大学计算机学院长沙410073

出　　处：《计算机科学》2013年第6期84-89,共6页Computer Science

基　　金：国家核高基重大专项课题(2011ZX03002-004-01)资助

摘　　要：上下文态势是将大规模、广地域范围内的上下文信息综合在一起形成的一种全局信息。随着各类具备感知能力的移动终端的普及,如何获取这种全局态势并利用态势来为用户提供更好的服务是亟待解决的问题。基于"端+云"相结合的计算模式,提出移动终端的统一抽象模型来实现上下文信息收集,进而提出了在云端对大规模上下文信息进行聚合、基于MapReduce计算模型的态势信息获取算法。通过一个大规模上下文管理框架对研究内容进行验证,并以一个交通态势实例验证了框架的有效性。The context situation refers the global view extracted from massive context information collected from a wide area. Along with the popularity of various mobile terminals with the ability to sense its context, it is a great problem to get this kind of situation and provide better service based on what we get. On the basis of the ＂terminal＋cloud＂ compu- ting paradigm, this paper proposed a unified abstract model for mobile terminals to realize context collections. And then, we proposed an algorithm to realize the aggregation of massive context information on the cloud side, which is based on the MapReduee computing pattern. We validated the research content of this paper by a large-scale context management framework as well as a traffic situation t application based on this framework.

关 键 词：上下文态势 软件抽象模型 聚合 MAPREDUCE 基于规则 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]