Improvements in technology to capture, manage, and store ever increasing amounts of electronic data within the oil and gas pipeline industry presents new challenges in discovering the meaning and relevancy of this data in the management of pipeline integrity. Companies expend a significant level of resources in integrity data management at the time of pipeline installation, during integrity assessment work and on-going compliance, maintenance and operational activities. The meaning or interpretation of this data is typically revealed through expert team based discussion, tabular and graphical reporting or the direct application of operator-defined hypothesis or queries to reveal knowledge. Over the last several years, new processes and techniques have been developed to discover relevancy in large, disparate databases as often found within pipeline operations. This paper will discuss one of these new processes for discovering knowledge from data.

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