A spatiotemporal model for ephemeral data in pervasive computing networks
Jonas Michel, Christine Julien, Jamie Payton, Gruia-Catalin Roman · 2012
Pervasive computing evokes a vision of digitally-accessible environments with which applications and users interact in localized ways. In this vision, information is ephemeral: it is created, moved, stored, and deleted on-demand at rapid rates. Without a formal data model that enables the data itself to speak about its spatial and temporal bearings, it is difficult to build support for accessing an information-rich digital world in a general-purpose way. In this paper, we demonstrate the need for an expressive data model of the inherently ephemeral data in pervasive computing and propose the beginnings of such a model that explicitly tags information with spatial and temporal semantics. Our model is founded on spatiotemporal trajectories, which capture the spatial and temporal semantics of data and the phenomenon it represents. We further demonstrate both the need for and potential impact of a general-purpose expressive spatiotemporal data model using several use cases.