Data interaction models for predictive system in pervasive computing environment

普适计算是一个由应用程序驱动的领域，正在缓慢地扩散到我们的日常生活中。由于各种应用程序集的多学科性质，以及缺乏标准定义、算法和体系结构，普及系统是复杂的。在短期和长期内以各种形式从许多设备收集数据，这些设备通常具有无线和移动功能。这些系统之间的数据交互需要谨慎的迭代方法。此外，大多数普及性应用程序仍处于初级阶段，大多存在于实验室或专门环境中，或以单个孤立应用程序的形式存在。因此，随着这些系统的成熟，解决公共数据交互模型以实现高效智能普适系统的研究是至关重要的。在无线网络能够在普适应用中实现外来设备之间的免配置数据交互之前，还需要做更多的工作。

Pervasive computing is an application-driven field which is slowly proliferating into our everyday life. Pervasive systems are complex due to the multidisciplinary nature of the diverse set of applications and lack of standard definitions, algorithms and architectures. Data is collected in various forms over short and long term period from a number of devices, often with wireless and mobile capabilities. The data interaction among these systems requires prudent iterative approach. Besides, most pervasive applications are still in a nascent stage and exist mostly in labs or in specialized environments or in the form of single isolated applications. Hence, research is crucial as these systems mature to address common data interaction models to achieve efficient intelligent pervasive systems. A lot more work is needed before wireless networking can enable configuration-free data interaction between alien devices in pervasive applications.