Amidu Oloso

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The performance and ease of extensibility for two Big-Data technologies, SciDB and Hadoop/MapReduce (HD/MR), are evaluated on identical hardware for an Earth science use case of locating intersections between two NASA remote sensing satellites’ ground tracks. SciDB is found to be 1.5 to 2.5 times faster than HD/MR. The performance of HD/MR approaches that(More)
We have implemented a flexible User Defined Operator (UDO) for labeling connected components of a binary mask expressed as an array in SciDB, a parallel distributed database management system based on the array data model. This UDO is able to process very large multidimensional arrays by exploiting SciDB's memory management mechanism that efficiently(More)
We have implemented an updated Hierarchical Triangular Mesh (HTM) as the basis for a unified data model and an indexing scheme for geoscience data to address the variety challenge of Big Earth Data. In the absence of variety, the volume challenge of Big Data is relatively easily addressable with parallel processing. The more important challenge in achieving(More)
Filesystems continue to be a major performance bottleneck for many applications across a variety of hardware architectures. Most existing attempts to address this issue, e.g., PVFS, rely upon system resources which are not typically tuned for any specific user application, whereas others rely on special hardware capabilities such as shared-memory.We have(More)
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