DC: Small: A Programming Model for Distributed Data Fusion

由于传感器的范围已扩展到不断捕获和/或传输各种类型数据的智能手机，因此数据正在实时生成。许多数据以不同的语义形式出现，需要加以区分。例如，推文和即时消息携带不同类型的信息，尽管两者都基于文本。这样的分布式数据携带需要被捕获和处理以用于在接近真实的时间中确定信息的状态的丰富语义，并且这提出了许多挑战。这项工作提出了一种基于语言（Java）的方法，沿着与一个移动的代理为基础的系统，以捕捉重要的属性，如及时性，货币，不完整性，一致性和自治性的数据利用其源的属性。此外，由于数据源是完全自主的，信息流策略决定了对非本地数据的访问控制。策略对数据访问和移动构成约束，并且在这种情况下存在多种处理可能性。实时执行这样的数据融合是非常具有挑战性的，它设想使用Java移动的代理框架。编译器分析的作用对于使运行时智能化以减少不适当的开销从而控制分布式真实的时间处理需求是至关重要的。建议在交通和导航系统等领域的几个应用程序上测试此软件基础设施。

Data is being generated everywhere in real-time since the scope of sensors has extended to smartphones that continually capture and/or transmit a varied type of data. A lot of data comes in different semantic forms that needs to be distinguished. For example, tweets and instant messages carry different types of information although both are based on text. Such distributed data carries rich semantics that need to be captured and processed for determining the state of the information in near real time and this poses many challenges. This work proposes a language-based (Java) approach along with a mobile agent based system to capture important properties such as timeliness, currency, incompleteness, consistency, and autonomy of data utilizing the properties of their sources. Moreover, since the data sources are quite autonomous, information flow policies dictate access control of the non-local data. Policies pose constraints on data access and movement and multiple processing possibilities exist in such a scenario. Performing such data fusion in real-time is very challenging and it is envisioned to use Java mobile agents framework. The role of compiler analysis is critical to make the runtime smart to reduce undue overhead to get distributed real time processing needs under control. It is proposed to test this software infrastructure on several applications from the domains such as the transportation, and the navigation systems.