Modeling the Internet of Things and People: A Foundational Approach
Spencer J. Breiner, Eswaran Subrahmanian, Ram D. Sriram
As we extend the reach of the Internet through sensing and automation, networked systems interact more and more of our daily lives, requiring much greater sensitivity to social networks and greater robustness in the face of human behavior. A tremendous amount of messy, heterogeneous data passes through such networks; integrating and applying that data so that it can be used for seamless interoperability and higher level reasoning requires conversion into some higher abstraction. To facilitate this conversation, we need standard information models which accurately capture objects, events, and other processes, and which support a variety of generic model operations such as linking, updating and scaling. In this paper, we argue that category theory, a branch of abstract mathematics, provides a firm conceptual foundation for information modeling which already meets most of these criteria. We close with a discussion of some challenges for exploiting category theory in applied contexts.
1st International Workshop on Semantic Interoperability for the IoT