Finalized:Monday, December 5, 2016
Author(s):Rilee, M. L., K. S. Kuo, T. Clune, A. Oloso, P. G. Brown and H. Yu
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 optimal value with a Big Data solution for Earth Science (ES) data analysis, however, is being able to achieve good scalability with variety. With HTM unifying at least the three popular data models, i.e. Grid, Swath, and Point, used by current ES data products, data preparation time for integrative analysis of diverse datasets can be drastically reduced and better variety scaling can be achieved. HTM is also an indexing scheme, and when applied to all ES datasets, data placement alignment (or co-location) on the shared nothing architecture, which most Big Data systems are based on, is guaranteed and better performance is ensured. With HTM most geospatial set operations become integer interval operations with further performance advantages.
M. L. Rilee, K. S. Kuo, T. Clune, A. Oloso, P. G. Brown and H. Yu, "Addressing the big-earth-data variety challenge with the hierarchical triangular mesh," 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, 2016, pp. 1006-1011. doi: 10.1109/BigData.2016.7840700This 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.