The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Data Redundancy and Duplicate Detection in Spatial Join Processing
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Pig latin: a not-so-foreign language for data processing
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Query processing of massive trajectory data based on mapreduce
Proceedings of the first international workshop on Cloud data management
SystemML: Declarative machine learning on MapReduce
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
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
CG_Hadoop: computational geometry in MapReduce
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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This demo presents SpatialHadoop as the first full-fledged MapReduce framework with native support for spatial data. SpatialHadoop is a comprehensive extension to Hadoop that pushes spatial data inside the core functionality of Hadoop. SpatialHadoop runs existing Hadoop programs as is, yet, it achieves order(s) of magnitude better performance than Hadoop when dealing with spatial data. SpatialHadoop employs a simple spatial high level language, a two-level spatial index structure, basic spatial components built inside the MapReduce layer, and three basic spatial operations: range queries, k-NN queries, and spatial join. Other spatial operations can be similarly deployed in SpatialHadoop. We demonstrate a real system prototype of SpatialHadoop running on an Amazon EC2 cluster against two sets of real spatial data obtained from Tiger Files and OpenStreetMap with sizes 60GB and 300GB, respectively.