It is found that data placement on the networked nodes of a cluster based on the shared-nothing architecture (SNA) should align in the physical (i.e. spatiotemporal) space for most geoscience Big Data analysis systems in order to minimize data movements and thus achieve optimal performance and efficiency. This is due to the fact that data analysis in geosciences predominantly requires spatiotemporal coincidence. If individual datasets are considered separately in their placement on the cluster nodes, these systems often have to move data between nodes when an analysis involves two or more datasets. In this paper, we first report our discoveries from a data placement alignment experiment with two Big Data technologies, SciDB and Spark+HDFS, and then elucidate some of the far-reaching implications of this discovery.
K. S. Kuo, A. Oloso, K. Doan, T. L. Clune and H. Yu, "Implications of data placement strategy to Big Data technologies based on shared-nothing architecture for geosciences," 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, 2016, pp. 7605-7607. doi: 10.1109/IGARSS.2016.7730983This material is based upon work supported by the National Science Foundation under Grant No. 1541043. Opinions, findings, conclusions or recommendations expressed are those of the authors and do not reflect the views of the NSF.