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Knowledge Discovery in Intelligence Analysis

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dc.contributor.authorButler, Patrick Julian Careyen
dc.contributor.committeechairRamakrishnan, Narenen
dc.contributor.committeememberYao, Danfeng (Daphne)en
dc.contributor.committeememberPolys, Nicholas F.en
dc.contributor.committeememberBoedihardjo, Arnold P.en
dc.contributor.committeememberNorth, Christopher L.en
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2014-06-04T08:00:22Zen
dc.date.available2014-06-04T08:00:22Zen
dc.date.issued2014-06-03en
dc.description.abstractIntelligence analysts today are faced with many challenges, chief among them being the need to fuse disparate streams of data, as well as rapidly arrive at analytical decisions and quantitative predictions for use by policy makers. These problems are further exacerbated by the sheer volume of data that is available to intelligence analysts. Machine learning methods enable the automated transduction of such large datasets from raw feeds to actionable knowledge but successful use of such methods require integrated frameworks for contextualizing them within the work processes of the analyst. Intelligence analysts typically distinguish between three classes of problems: collections, analysis, and operations. This dissertation specifically focuses on two problems in analysis: i) the reconstruction of shredded documents using a visual analytic framework combining computer vision techniques and user input, and ii) the design and implementation of a system for event forecasting which allows an analyst to not just consume forecasts of significant societal events but also understand the rationale behind these alerts and the use of data ablation techniques to determine the strength of conclusions. This work does not attempt to replace the role of the analyst with machine learning but instead outlines several methods to augment the analyst with machine learning. In doing so this dissertation also explores the responsibilities of an analyst in evaluating complex models and decisions made by these models. Finally, this dissertation defines a list of responsibilities for models designed to aid the analyst's work in evaluating and verifying the models.en
dc.description.degreePh. D.en
dc.format.mediumETDen
dc.identifier.othervt_gsexam:2920en
dc.identifier.urihttp://hdl.handle.net/10919/48422en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectData miningen
dc.subjectintelligence analysisen
dc.subjectdeshreddingen
dc.subjectforecastingen
dc.titleKnowledge Discovery in Intelligence Analysisen
dc.typeDissertationen
thesis.degree.disciplineComputer Science and Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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