Avineon proposes to develop an intelligence gathering system to identify and monitor the knowledge gained from the integration of system interoperability management data (Risk analysis, BF Comparisons, Metrics, DMSAR, Program TRs, BF CM data, BF ECM analysis) with mission analysis, system models, requirements, network models, and functional analyses. This effort will also document knowledge management processes that exist or are required for effective utilization of knowledge resulting from a combination of multiple BF systems. Avineon will define a generalized knowledge architecture including the structure of the data warehouse, interfaces to data sources, operational processes, affected user groups, and responsible organizational entities which can be developed into an XML-based data warehouse to serve as the foundation of a knowledge-based system architecture that captures system configuration, certification, operational, and test data along with a description of the organizational groups and processes that utilize this information and data. Object oriented analysis and design methods will be used, following the Rational Unified Process (RUP). Requirements, object models and their interactions will be described using UML and Rose visual models. As part of the process, hardware, network, and COTS product components will be evaluated and selected to suggest a working system representing the selected architecture. The results of this research will enable the BF community to define how the information gained from improved interoperability can be easily and quickly analyzed to provide knowledge that enables decision makers to decide where and how improvements can be made and to determine how the new knowledge can be created or gained in a systematic fashion. Moreover, heterogeneous data sources have traditionally posed problems for mission critical analysis of combat data, thereby reducing the effectiveness of collecting such data. Since data resides on several disparate systems, real time access, and aggregation of that data is difficult. Results of the proposed work will enable the development of knowledge systems with the ability to analyze data and information, pose questions regarding performance and requirements improvements, and recommend where and how efforts can be made to improve overall performance. Applying this technology to combat data will enable battle readiness assessment in mission critical situations. Since the competitive environments in which most corporations operate also require prompt integration and analysis of data from multiple sources, the research proposed herein also has strong potential for successful commercial application
