Session details: Special Issue on Big Spatial Data (Part 2)

Abstract

This issue is Part 2 of SIGSPATIAL Special issue on Spatial Big Data. The issue includes five interesting articles. The first two articles present two approaches for MapReduce-based systems built to support Big Spatial Data. In particular, the first article by Eldawy and Mokbel presents the ecosystem of SpatialHadoop, which includes the SpatialHadoop engine with a built-in support for spatial data, the Pigeon spatial language, and a visualization layer. The second article by Wang et al. presents the Hadoop-GIS system; a scalable and high performance spatial data warehousing system for large scale spatial queries, equipped with GPU-based geometric algorithms integrated into the MapReduce pipeline. The next two articles focus on using GPUs as a means of accelerating large-scale spatial data processing. In particular, in the third article, Prasad et al. lay out a vision for accelerating geo-spatial computations and analytics using a combination of shared and distributed memory platforms,with GPUs and hundreds to thousands processing cores. The fourth article by Zhang et al. presents data parallel designs on GPU-accelerated clusters for spatial indexing, spatial joins, and various other spatial operations. The issue is concluded with an article by Bhaduri et al., discussing various trending applications of big data at Oak Ridge National Lab (ORNL). This concludes the second part of the special issued on Big Spatial Data; all composed of ten interesting and thought-provoking articles. I would like to sincerely thank all the authors for their invited contributions.