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51318-11007-Data Integration Techniques to Support Meaningful Visualization of Corrosion Data

Specifically, the paper examines the key first step in visualizing corrosion data: integrating different types of data from different sources into a single system by using spatial components within each dataset to correlate the information.

Product Number: 51318-11007-SG
Author: Joshua Greer
Publication Date: 2018
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The advent of more advanced methods for monitoring corrosion means that, for most pipeline professionals, decisions are not constrained by a lack of data. If anything, the sheer volume of information available can prove to be a bottleneck, as the amount of time required for analysis grows with the amount of data available. This paper focuses on how pipeline corrosion control departments, integrity management personnel and administrators can obtain a more nuanced view of their corrosion data by using visualization techniques to quickly analyze large datasets. Specifically, the paper examines the key first step in visualizing corrosion data: integrating different types of data from different sources into a single system by using spatial components within each dataset to correlate the information. It will explore the types of data that are well suited for data interactivity analysis, as well as best practices for collecting this data and preparing it for the visualization process.

Key words: Data Visualization, Data Analysis, Data Mining, Data Integration, Integrity Management, Corrosion Control, Pipeline, Cathodic Protection

The advent of more advanced methods for monitoring corrosion means that, for most pipeline professionals, decisions are not constrained by a lack of data. If anything, the sheer volume of information available can prove to be a bottleneck, as the amount of time required for analysis grows with the amount of data available. This paper focuses on how pipeline corrosion control departments, integrity management personnel and administrators can obtain a more nuanced view of their corrosion data by using visualization techniques to quickly analyze large datasets. Specifically, the paper examines the key first step in visualizing corrosion data: integrating different types of data from different sources into a single system by using spatial components within each dataset to correlate the information. It will explore the types of data that are well suited for data interactivity analysis, as well as best practices for collecting this data and preparing it for the visualization process.

Key words: Data Visualization, Data Analysis, Data Mining, Data Integration, Integrity Management, Corrosion Control, Pipeline, Cathodic Protection

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