rigger detection using geographical relation graph for social context awareness
rigger detection using geographical relation graph for social context awareness
复制标题
使用地理关系图进行操纵者检测以实现社会情境感知
DOI：
10.1007/s11036-012-0398-7
复制
发表时间：
2012
期刊：
影响因子：
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通讯作者：
T. Takahashi
中科院分区：
文献类型：
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作者：
T. Nishio;R. Shinkuraa;F. D. Pellegrini;H. Kasai;K. Yamaguchi;T. Takahashi
上下文感知的概念被认为是云计算平台和智能手机操作系统带来的新的无处不在的网络服务范式的关键推动者。特别是，自主的基于上下文的服务定制正在成为这种情况下的基本工具，因为不能指望用户通过手动和显式地匹配当前上下文的首选项来逐步选择适当的网络服务。因此，在这项工作中，我们将重点关注如何检测网络服务上下文变化的核心问题。反过来，检测到这些更改可以及时触发系统重新配置。我们引入了一种基于混合图表示模型的触发检测机制，该模型能够对人和社会对象（如商店、餐馆和事件点）之间的地理和社会关系进行编码。当图表发生重大变化时，我们的机制会产生一个触发器，并且它能够呈现地理关系中的重大变化，这些关系保持在彼此社会联系的对象之间。我们的方法的主要优点是：(1)它不需要提前建立参考模型，(2)一旦定义了图，它就可以统一地处理不同类型的社会对象。计算机模拟场景为我们的方法的预期性能提供了证据。
The concept of context awareness is believed to be a key enabler for the new ubiquitous network service paradigm brought by cloud computing platforms and smartphone OSs. In particular, autonomous context-based service customization is becoming an essential tool in this context because users cannot be expected to pick step by step the appropriate network services by manually and explicitly matching preferences for their current context. In this work, we hence focus on the core problem of how to detect changes of context for network services. In turn, detection of such changes can trigger timely system reconfigurations. We introduce a trigger detection mechanism based on a mixed graph-based representation model able to encode geographical and social relationships among people and social objects like stores, restaurants, and event spots. Our mechanism generates a trigger when a significant change in the graph takes place, and it is able to render significant changes in a geographical relationship that holds among objects socially connected with each other. The main benefits of our method are that (1) it does not require building reference models in advance, and (2) it can deal with different kinds of social objects uniformly once the graph is defined. A computer simulation scenario provides evidence on the expected performance of our method.