Optimizing Big Data Analytics
Mining Massive Trajectory Data
Massive trajectory data from various sensors bring new opportunities of examining social and environmental processes at a fine spatial and temporal scale. Processing such massive data is computationally challenging. By designing a combinatorial bounding box and min-distance approach to process K Best Connected Trajectory query (K-BCT), trajectory data mining process can be significantly optimized.
Efficnet Map Reprojection
Geospatial transformations in the form of reprojection calculations for large datasets can be computationally intensive; as such, finding better, less expensive ways of achieving these computations is desired. We have developed a Compute Unified Device Architecture (CUDA)-based parallel algorithm to perform map reprojections for raster datasets on personal computers using Graphics Processing Units (GPUs). This algorithm has two unique features: a) an output-space-based parallel processing strategy to handle transformations more rigorously, and b) a chunk-based data decomposition method for projected space in conjunction with an on-the-fly data retrieval mechanism to avoid memory overflow.
