System Self-Awareness and Related Methods for Improving the Use and Understanding of Data within DoD
Mackinnon, Douglas J.
Gallup, Shelley P.
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Data sources for DoD applications often include disparate realtime sensor and archival sources with multiple dimensions, as well as very high rates and very large volumes. The data can include structured data that are of traditional forms (for example, stored in relational databases, Excel, or XML files with well-defined labels with meta-data). Unstructured data can also be included, such as free text, Word, PDF, PowerPoint, and emails. A large percentage of data are unstructured. It remains a daunting task to retain logical integrity of the separate data sources, and support multiple parallel and asynchronous functions of storage, analysis, search, and retrieval of these data sources. Analysts, at present, must manually comb through immense volumes of multisource, multiclassification-level intelligence data to find previously unknown and undiscovered patterns, associations, relationships, trends, and anomalies. This effort can be tedious and slow, and can depend largely upon the individual analyst's experience. Without accurate and timely front-end analysis, link analysis and inferences about future trends are not possible. The authors' methodology cross-examines and considers all of the data to create a full picture, and thus gain improved system self-awareness. Analysts and the intelligence community could benefit from automated, scalable, and robust tools and methods to analyze such large data sets quickly and thoroughly, to create and sustain situational awareness in real time.
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