As a result of foresight and planning that has established a logical data model (i.e., the Master Model), a set of standard reference tables and 17 migration software systems as the foundation of a disciplined Corporate Data Environment (CDE), USTRANSCOM now finds itself in the enviable position of being able to implement DoD’s vision of a Global Information Grid (GIG) with speed and confidence. The urgency with which DoD views the need to shift the burden of tedious data filtering and interpretation tasks from human operators to automated machine-based processes, is reflected in the CDE and provides the opportunity for USTRANSCOM to take a leading role in implementing the GIG vision.

USTRANSCOM is inundated with an overwhelming volume of data from a proliferation of heterogeneous internal and external sources. While virtually all of these data are captured and stored electronically, they are largely devoid of context and therefore have to be interpreted and transformed into useful information by human operators, planners and decision makers. The penalties associated with such a human-intensive data-centric environment include data bottlenecks, aged data, breakdown of data exchange interfaces, and the inability to accurately interpret and analyze data within time-critical constraints. This places USTRANSCOM in a reactive mode, and forces the expenditure of valuable resources on treating symptoms rather than addressing the core problem.

The report outlines a blueprint for extending USTRANSCOM’s existing Corporate Data Environment (CDE) to an information-centric knowledge management environment that provides a vehicle for making information and knowledge explicit and accessible throughout the organization. The Corporate Information-Centric Environment (CICE) architecture is proposed as a core solution to support: (1) the automatic filtering of data by placing data into information context; (2) the automated reasoning of software agents as they monitor events and assist human users in planning, replanning and decision-making tasks; and, (3) autonomic computing capabilities that allow systems to monitor, diagnose and troubleshoot themselves. The principal objective of CICE is to shift the burden of lower level data interpretation, filtering, and context building, from human operators to automated software-based processes.


Software Engineering



URL: https://digitalcommons.calpoly.edu/cadrc/20