DC: Small: A Programming Model for Distributed Data Fusion

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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.