2015/03/10 by Reem Y. Ali, Venkata M. V. Gunturi, Shashi Shekhar · 1 citation
Computer Science · Social Sciences · Engineering · #Data Management and Algorithms #Human Mobility and Location-Based Analysis #Traffic Prediction and Management Techniques
paper · doi:10.1145/2744700.2744703
openalex publication_date 2015/03/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/26
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.