EC Oceanography

The incentive for this article is to design a virtual environment that fosters the use of uniform and nonuniform gridding tactics to handle large-scale bathymetry data. The understanding of depth and sonar imagery has been a growing interest of many oceanographers and scientists, hence proper handling of massive sonar data could improve coverage of the sea terrain. The bathymetry data will be acquired from the World War II U.S. Navy Liberty Vessels sunken in the Gulf of Mexico. The implementation of these non-uniform gridding tactics should prove worthwhile, allowing finer resolution and approximation of the data set while utilizing a lower dimension of the data set. Heuristic terrain simplification algorithms are based upon the fundamentals of divide and conquer algorithms in greedy programming. The simplification method proposed in this work is a multi-pass decimation method, which begins with a Delaunay triangulation of the input data of 167,358 triangles and then a reduction to 83,679 triangles to remove outliers. Hence, the results of the heuristic terrain simplification algorithm produced a significant 50% percent compression ratio with optimal connectivity of the bathymetric data and still produced an accurate approximation of the bathymetric data set. The estimation of the terrain simplification algorithm granted exemplary results with respect to triangular compression, where in the original image was composed of 167,358 triangles was easily parsed into the Hadoop Ecosystem with Apache Spark Directed Acyclic Graph Engine (DAG) with Apache Scala utilization to formulate Resilient Distributed Data Sets for Amazon AWS Apache Cassandra (NoSQL Databases) to demonstrate quintessential optimization and scalability for sonar bathymetric data compression.

Keywords: Bathymetry; Delaunay Triangulation; Minimal Spanning Trees; Uniform Gridding; Hadoop Ecosystem; Apache Spark Directed Acyclic Graph(s) DAG; Apache Scala; Amazon AWS; Apache Cassandra

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