Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoStreaming
Efficient parallel kNN joins for large data in MapReduce
Proceedings of the 15th International Conference on Extending Database Technology
Efficient processing of k nearest neighbor joins using MapReduce
Proceedings of the VLDB Endowment
Future Generation Computer Systems
MobiS: a distributed paradigm of mobile sensor data analytics for evaluating environmental exposures
Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
High-resolution spatial interpolation on cloud platforms
Proceedings of the 28th Annual ACM Symposium on Applied Computing
CG_Hadoop: computational geometry in MapReduce
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Hadoop GIS: a high performance spatial data warehousing system over mapreduce
Proceedings of the VLDB Endowment
| Hi-index | 0.00 |
Geospatial queries (GQ) have been used in a wide variety of applications such as decision support systems, profile-based marketing, bioinformatics and GIS. Most of the existing query-answering approaches assume centralized processing on a single machine although GQs are intrinsically parallelizable. There are some approaches that have been designed for parallel databases and cluster systems, however, these only apply to the systems with limited parallel processing capability, far from that of the cloud-based platforms. In this paper, we study the problem of parallel geos patial query processing with the MapReduce programming model. Our proposed approach creates a spatial index, Voronoi diagram, for given data points in 2D space and enables efficient processing of a wide range of GQs. We evaluated the performance of our proposed techniques and correspondingly compared them with their closest related work while varying the number of employed nodes.