Ms. Alisha W. Mauck Profile

Alisha W. Mauck

Associate at Booz Allen Hamilton

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Proceedings Article | 3 May 2007 Paper

Quantifying an imagery system's performance with transformational mission data analysis

Alisha Mauck

Proceedings Volume 6555, 65550Z (2007) https://doi.org/10.1117/12.718648

KEYWORDS: Surveillance, Intelligence systems, Data analysis, Databases, Associative arrays, Data modeling, Systems modeling, Computing systems, Data processing, Image analysis

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Traditionally, the performance of an imagery intelligence collection system is quantified by a satisfaction percentage. The mission satisfaction is the number of images collected divided by the number of images requested. This paradigm assumes the information needed is generated from the collected imagery data if the data is delivered on time to the consumer. As persistent surveillance requirements become more prominent, the time sequence of data collection is increasingly important. The satisfaction percentage is not wholly descriptive of a collection system's ability to complete persistent surveillance missions. A metric of imagery data utility that is dependent on the time sequence of data collected is necessary. Booz Allen Hamilton's transformational mission analysis focuses on additional metrics to characterize satisfaction of persistent surveillance requirements. Surveillance missions are based on a need to monitor an activity or event. The observables are animate, and may require a time sequence of images. For surveillance imagery data to be useful, the system must collect the data in required sequence and deliver the information in a timely fashion. Booz Allen defines a utility score to quantify system performance against persistent surveillance missions. The utility score includes the satisfaction percentage, but is sensitive to the time dependences of data. This paper outlines a transformational approach to mission analysis. The paper introduces examples of surveillance missions, and the limited value of satisfaction percentage. It defines data relationships between imagery system capabilities and surveillance missions. Finally, it computes the utility score, and quantifies the performance of an example collection system.

Proceedings Article | 22 May 2006 Paper

Verifying end-to-end system performance with the transformational information extraction model

Alisha Mauck, Greg Roszyk

Proceedings Volume 6227, 62270L (2006) https://doi.org/10.1117/12.665065

KEYWORDS: Data modeling, Image processing, Systems modeling, Image analysis, Detection and tracking algorithms, Satellite imaging, Satellites, Data processing, Performance modeling, Databases

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In the intelligence community, the volume of imagery data threatens to overwhelm the traditional process of information extraction. Satellite systems are capable of producing large quantities of imagery data every day. Traditionally, intelligence analysts have the arduous task of manually reviewing satellite imagery data and generating information products. In a time of increasing imagery data, this manual approach is not consistent with the goal of a timely and highly responsive system. These realities are key factors in Booz Allen Hamilton's transformational approach to information extraction. This approach employs information services and value added processes (VAP) to reduce the amount of data being manually reviewed. Booz Allen has utilized a specialization/generalization hierarchy to aggregate hundreds of thousands of imagery intelligence needs into sixteen information services. Information Services are automated by employing value added processes, which extract the information from the imagery data and generate information products. While the intelligence needs and information services remain relatively static in time, the VAP's have the ability to evolve rapidly with advancing technologies. The Booz Allen Transformational Information Extraction Model validates this automated approach by simulating realistic system parameters. The functional flow model includes image formation, three information services, six VAP's, and reduced manual intervention. Adjustable model variables for VAP time, VAP confidence, number of intelligence analyst, and time for analyst review provide a flexible framework for modeling different system cases. End-to-End system metrics such as intelligence need satisfaction, end-to-end timeliness, and sensitivity to number of analyst and VAP variables quantify the system performance.

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