Innovative Data Anomaly Detection and Transformation for Analysis Applications Navy SBIR FY2013.2 Sol No.: Navy SBIR FY2013.2 Topic No.: N132-096 Topic Title: Innovative Data Anomaly Detection and Transformation for Analysis Applications Proposal No.: N132-096-0726 Firm: Frontier Technology, Inc. 75 Aero Camino, Suite A Goleta, California 93117 Contact: Joel Luna Phone: (937) 429-3302 Web Site: www.fti-net.com Abstract: The overall objective of this topic is to develop a software toolset to transform extracted data from different database systems and convert it into data packages that create model specific input files that support future modeling, simulation, and analysis tasks. Specifically, the main objective of this proposed Phase I research effort is to develop the concept for a new capability that automates, to the greatest degree practicable, a data transformation process that includes data analysis and validation and results in the creation of model input files that can be used to conduct required analyses. The result of this research is to identify the capabilities and requirements of a data transformation process that starts with data extracted from standard Navy data systems and produces valid model inputs for use in specified models and simulations. In particular, the process and tools required to check the data for errors and inconsistencies, automated means to perform such checks and provide insight into any errors or inconsistencies detected, as well as resolution of those errors or inconsistencies will be addressed. Benefits: This research is applicable to DoD and commercial industries employing complex maintenance, repair and supply models. These organizations must all adopt more streamlined methods and technologies to make their model input processes more efficient. The solution that these organizations need must address the data transformation problems of model input creation that can be adaptable, flexible, and easily integrated into existing and new models, and management and data systems. This capability must be able to be implemented within the confines of the available budget and to preclude operational impacts that could jeopardize mission readiness, as well as the financial bottom line. A data transformation toolset that will accurately assess data input issues and conditions and enable rapid determination of data input errors and inconsistencies at the record, item, TMS, and fleet levels, and even across the enterprise, will enable users to perform their model input preparation processes more efficiently compared to current capabilities. Return