Daytona and the fourth-generation language Cymbal
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Fundamentals of Parallel Processing
Fundamentals of Parallel Processing
A quadtree approach to domain decomposition for spatial interpolation in grid computing environments
Parallel Computing - Special issue: High performance computing with geographical data
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Bigtable: A Distributed Storage System for Structured Data
ACM Transactions on Computer Systems (TOCS)
Grid computing of spatial statistics: using the TeraGrid for G i*(d) analysis
Concurrency and Computation: Practice & Experience - Grids and Geospatial Information Systems
Mars: a MapReduce framework on graphics processors
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
MRGIS: A MapReduce-Enabled High Performance Workflow System for GIS
ESCIENCE '08 Proceedings of the 2008 Fourth IEEE International Conference on eScience
A theoretical approach to the use of cyberinfrastructure in geographical analysis
International Journal of Geographical Information Science
Experiences on Processing Spatial Data with MapReduce
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Spatial Queries Evaluation with MapReduce
GCC '09 Proceedings of the 2009 Eighth International Conference on Grid and Cooperative Computing
Phoenix rebirth: Scalable MapReduce on a large-scale shared-memory system
IISWC '09 Proceedings of the 2009 IEEE International Symposium on Workload Characterization (IISWC)
Proceedings of the ACM SIGSPATIAL Second International Workshop on High Performance and Distributed Geographic Information Systems
CudaGIS: report on the design and realization of a massive data parallel GIS on GPUs
Proceedings of the Third ACM SIGSPATIAL International Workshop on GeoStreaming
| Hi-index | 0.00 |
Managing and analyzing massive spatial datasets as supported by GIS and spatial analysis is becoming crucial to geospatial problem-solving and decision-making. MapReduce provides a data-centric computational model through which highly scalable spatial analysis computation can be achieved. However, it is challenging to leverage multi-dimensional spatial characteristics on the horizontally-partitioned and transparently managed MapReduce data system for improving the computational performance of spatial analysis. This paper tackles this challenge through the development of MapReduce-based computation of Gi*(d) -- a spatial statistic for detecting local clustering. Without exploiting spatial characteristics, Gi*(d) computation for a particular location requires pair-wise distance calculation for all points of a given dataset. A spatial locality-based storage and indexing strategy is developed to associate spatial locality with storage locality on MapReduce platform. Based on a spatial indexing method, unnecessary map tasks can be eliminated for a MapReduce job, thus significantly improving the overall computation performance. To leverage underlying parallelism on storage nodes, an application-level load balancing mechanism is developed to produce even loads among map tasks based on adaptive spatial domain decomposition. Experiments show the effectiveness of the developed storage and indexing strategy with different distance parameter settings. Significant reduction on execution time for all-point computation is observed through the use of the application-level load balancing mechanism.