Abstract
The size, variety, and update rate of spatial datasets are increasingly exceeding the capacity of commonly used spatial computing technologies to learn, manage, and process the data with reasonable effort. We refer to these datasets as Spatial Big Data (SBD). Examples of emerging SBD datasets include temporally detailed (TD) roadmaps that provide speeds every minute for every road-segment, GPS track data from cell-phones, and engine measurements of fuel consumption, greenhouse gas (GHG) emissions, etc. Harnessing SBD has a transformative potential. For example, a 2011 McKinsey Global Institute report estimates savings of "about $600 billion annually by 2020" in terms of fuel and time saved by helping vehicles avoid congestion and reduce idling at red lights or left turns. In this paper, we discuss the challenges posed by SBD for a next generation of routing services and we present our work towards addressing these challenges.
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