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dc.contributor.authorButler, Patrick Julian Careyen_US
dc.date.accessioned2014-06-04T08:00:22Z
dc.date.available2014-06-04T08:00:22Z
dc.date.issued2014-06-03en_US
dc.identifier.othervt_gsexam:2920en_US
dc.identifier.urihttp://hdl.handle.net/10919/48422
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_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis Item is protected by copyright and/or related rights. Some uses of this Item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectData miningen_US
dc.subjectintelligence analysisen_US
dc.subjectdeshreddingen_US
dc.subjectforecastingen_US
dc.titleKnowledge Discovery in Intelligence Analysisen_US
dc.typeDissertationen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Science and Applicationsen_US
dc.contributor.committeechairRamakrishnan, Narendranen_US
dc.contributor.committeememberYao, Danfengen_US
dc.contributor.committeememberPolys, Nicholas Fearingen_US
dc.contributor.committeememberBoedihardjo, Arnold P.en_US
dc.contributor.committeememberNorth, Christopher L.en_US


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