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Increasingly, location-aware datasets are of a size, variety, and update rate that exceeds the capability of spatial computing technologies. This paper addresses the emerging challenges posed by such datasets, which we call Spatial Big Data (SBD). SBD examples include trajectories of cellphones and GPS devices, vehicle engine measurements, temporally(More)
Given a spatio-temporal network, a source, a destination, and a start-time interval, the All-start-time Lagrangian Shortest Paths (ALSP) problem determines a path set which includes the shortest path for every start time in the given interval. ALSP is important for critical societal applications related to air travel, road travel, and other spatio-temporal(More)
Given a set of operators and a spatio-temporal network, the goal of the Storing Spatio-Temporal Networks (SSTN) problem is to produce an efficient data storage method that minimizes disk I/O access costs. Storing and accessing spatio-temporal networks is increasingly important in many societal applications such as transportation management and emergency(More)
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Given a graph and a set of service center nodes, a Capacity Constrained Network-Voronoi Diagram (CCNVD) partitions the graph into a set of contiguous service areas that meet service center capacities and minimize the sum of the shortest distances from graph-nodes to allotted service centers. The CCNVD problem is important for critical societal applications(More)
Given a transportation network with weighted nodes (e.g., population, resource demand) and a set of service centers (e.g., gas stations) with capacity constraints, a Capacity Constrained Network-Voronoi Diagram (CCNVD) partitions the network such that load imbalance across service centers is minimized. A CCNVD is important for resource allotment during(More)