Contemporary Computing Technologies for Processing Big Spatiotemporal Data

Abstract

Geographic phenomena evolve in a four-dimensional spatiotemporal world. To capture the geographical phenomena at different scales, large amount of data (big data) are produced with specific spatiotemporal patterns. Phenomena evolution and the principles driving the evolution provide pathways for developing methodological solutions to process the big spatiotemporal data. Based on experiences gained from several projects, such as climate studies and cloud computing, we introduce in this chapter modern computing technologies required for processing big data, including (1) sensor web, Earth observations, and model simulations for collecting and generating big data, (2) flexible and standard-based systems for managing big data for easy discovery and access, (3) multidimensional visual analytics for exploring and analyzing big spatiotemporal data, and (4) grid, cloud, and GPU computing for addressing the computing intensive challenges. We discuss through exemplar projects how these cutting-edge computing technologies are utilized to handle big spatiotemporal data. We expect this chapter to set a computing research context for future big data handling at different spatiotemporal granules.