Publications

Our research is continually advancing the field of bathymetric data processing, and we will share the results of our efforts as they develop. Please check back often for the latest publications.

3 publications found

Technicalities ... A Cloud Optimized Method for Processing Bathymetric Data

Authors: Wright, D., Wright, C.

2020 (2020)

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Abstract

This paper introduces a novel cloud-optimized methodology for processing bathymetric data collected from hydrographic surveys. By leveraging cloud computing resources, we demonstrate significant improvements in processing speed and scalability compared to traditional on-premise solutions. The proposed method utilizes parallel processing techniques and efficient data storage strategies to handle large datasets effectively.

The Geopoint Project: Cloud Optimized Processing for Hydrographic Data

Authors: Wright, D.

2019 (2019)

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Abstract

The cloud computing environment presents an opportunity to develop hydrographic data processing algorithms optimized to perform using a parallel processing architecture. The primary rationale for developing these API's is to accelerate the data processing rate, as well as minimally provision the necessary resources. The Geopoint API has been designed to operate using multiple compute nodes and can be provisioned as needed in quantity and speed of processors. An efficient solution of this type brings with it questions of job partitioning, storage and retrieval, reliability, and robustness. This study considers the architectural elements, the computational resources needed and a comparison to the corresponding NOAA Bag file.

A Cloud based Solution in Hydrographic Data Processing: The Shift to a Web Centric Software Platform

Authors: Wright, D., Wright, C.

2017 (2017)

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Abstract

Currently available hydrographic data processing software is mostly limited to on premises installations, requiring annual licenses and a significant investment in hardware and data storage. This imposes hardware limitations on both the speed and capacity of processing large datasets. By leveraging the tools available through cloud computing, a Software as a Service (SaaS) data processing model is proposed. As implemented, many of the limitations imposed by on premises architecture are eliminated, but many new challenges are expected in bringing a SaaS processing solution to the field of hydrographic data processing. Using academically proven Open Source API's to build the conversion engine and create the requisite Bag output files, we will show how a cloud based solution accomplishes these tasks more efficiently and with a significant reduction in both time and cost over traditional on premise software. The model requires rigorous testing methodologies as well as the development of a secure and reliable web based interface. It will also be shown that the cloud architecture provides additional opportunities for the use of aggregated data to satisfy the evolving needs of chart producing organizations. With these concepts in mind, it is intended to demonstrate the functionality and benefits of the proposed processing system.