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The Joint Force’s ability to integrate data and systems at speed and scale is of utmost importance to maintain a competitive edge in the data-driven arena of 21st century warfare. The current methods employed by developers to find system integration points, however, are ad-hoc, predominantly manual, and too costly – consider the explosion in volume and heterogeneity of the underlying databases that support modern-day operations. In this work, we present a data archeology and alignment workbench, known as Semantic Lifting of Integrated Messages (SLIM), that allows developers to quickly discover and document commonalities among disparate data. Our novel workbench uses background knowledge to constrain the geometric orientation of unfamiliar data in ways that are meaningful to developers. To accomplish this, SLIM uses a variety of Machine Learning algorithms to unify and characterize the meaning of data, and a dashboard of linked visualizations to convey semantic commonalities from different perspectives and dimensions. Additionally, the workbench maintains a web of execution provenance that developers can use to trace and probe results in order to assess relevancy and meaningfulness. As a demonstration, we describe two studies that use our process to quickly find non-trivial commonalities among multiple datasets, including the Universal Command and Control Interface (UCI) and an ontology about the arrival and departure of vehicles.
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