Fractals for secondary key retrieval
PODS '89 Proceedings of the eighth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
The design and analysis of spatial data structures
The design and analysis of spatial data structures
Using Hilbert curve in image storing and retrieving
Information Systems
Parallel netCDF: A High-Performance Scientific I/O Interface
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
A Web Service Model for Climate Data Access on the Grid
International Journal of High Performance Computing Applications
Map-reduce-merge: simplified relational data processing on large clusters
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Column-stores vs. row-stores: how different are they really?
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A comparison of approaches to large-scale data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Column-oriented storage techniques for MapReduce
Proceedings of the VLDB Endowment
The parallel system for integrating impact models and sectors (pSIMS)
Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery
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We propose a parallel method to accelerate the performance of subset queries on raster images. The method, based on map-reduce paradigm, includes two principles from database management systems to improve the performance of subset queries. First, we employ column-oriented storage format for storing locationand weather variables. Second, we improve data locality by storing multidimensional attributes such as space and time in a Hilbert order instead of a serial, row-wise order. We implement the principles in a map-reduce environment, maintaining compatibility with the replication and scheduling constraints. We show through experiments that the techniques improve data locality and increase performance of subset queries, respectively, by 5x and 2x.