Modeling & Simulation (M&S) is the keystone of effective conduct of Live-Virtual-Constructive events across the spectrum of M&S community activities (training, test & evaluation, experimentation, acquisition, etc.). The widespread and efficient application of M&S technology can become more effective and cost-efficient by leveraging past M&S investments. As noted in the Live-Virtual-Constructive Common Capabilities: Asset Reuse Mechanisms Implementation Plan (Riggs 2010), one way of accomplishing this is through discovery and reuse of extant M&S concepts of operations (CONOPs), data, scenarios, and lessons-learned. With available data elements numbering in the tens of thousands, however, simple search techniques do not provide the technical sophistication necessary to find the right data, if it exists at all. There is a need to provide the incentive and reward necessary to outweigh the difficulty of potential reuse. To reverse this situation, the Department of Defense (DoD) has to make better use of structured search techniques that are currently being employed by organizations such as Google and Facebook. These techniques can extract ‘context’ from the search data or can apply graphic search techniques to develop purpose driven ‘relationships,’ and may be the difference between making reuse a viable option or perpetuating redundant development and inefficient use of data.

This paper explores the re-use proposition in the DoD modeling and simulation community. Discovery metadata attributes, data quality indices, and search technologies for M&S data are explored in representative use cases (including a business-case for the archiving, attribution, and discovery of priority M&S data). It also explores the applicability of existing standards and specifications like Amazon's OpenSearch, as well as the potential for leveraging current DoD efforts (e.g. Intelligence Community and Department of Defense Content Discovery and Retrieval Integrated Project Team) that support the potential business-case.