A Hybrid Model and Computing Platform for Spatio-semantic Trajectories

Spatio-temporal data management has progressed significantly towards efficient storage and indexing of mobility data. Typically such mobility data analytics is assumed to follow the model of a stream of (x,y,t) points, usually coming from GPS-enabled mobile devices. With large-scale adoption of GPS-driven systems in several application sectors (shipment tracking to geo-social networks), there is a growing demand from applications to understand the

spatio-semantic

behavior of mobile entities. Spatio-semantic behavior essentially means a semantic (and preferably contextual) abstraction of raw spatio-temporal location feeds. The core contribution of this paper lies in presenting a

Hybrid Model

and a

Computing Platform

for developing a semantic overlay - analyzing and transforming raw mobility data (GPS) to meaningful semantic abstractions, starting from raw feeds to semantic trajectories. Secondly, we analyze large-scale GPS data using our computing platform and present results of extracted spatio-semantic trajectories. This impacts a large class of mobile applications requiring such semantic abstractions over streaming location feeds in real systems today.