{"548551":{"#nid":"548551","#data":{"type":"event","title":"PhD Defense by Mahmoud A. Abdelaal","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EPh.D. Thesis Defense\u003C\/strong\u003E\u003C\/p\u003E\u003Cp align=\u0022center\u0022\u003E\u003Cstrong\u003E\u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\u003Cp align=\u0022center\u0022\u003EBy\u003C\/p\u003E\u003Cp align=\u0022center\u0022\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp align=\u0022center\u0022\u003EMahmoud A. Abdelaal\u003C\/p\u003E\u003Cp align=\u0022center\u0022\u003E(Advisor: Prof. Dimitri N. Mavris)\u003C\/p\u003E\u003Cp align=\u0022center\u0022\u003E9:00AM, Wednesday, July 06, 2016\u003C\/p\u003E\u003Cp align=\u0022center\u0022\u003E\u003Cem\u003EWeber Space Science and Technology Building (SST-II)\u003C\/em\u003E\u003C\/p\u003E\u003Cp align=\u0022center\u0022\u003E\u003Cem\u003ECollaborative Visualization Environment (CoVE)\u003C\/em\u003E\u003C\/p\u003E\u003Cp align=\u0022center\u0022\u003E\u003Cem\u003E\u0026nbsp;\u003C\/em\u003E\u003C\/p\u003E\u003Cp align=\u0022center\u0022\u003E\u003Cstrong\u003ECLUSTER ANALYSIS FOR DECISION-POINT RECOGNITION\u003C\/strong\u003E\u003C\/p\u003E\u003Cp align=\u0022center\u0022\u003E\u003Cem\u003E\u0026nbsp;\u003C\/em\u003E\u003C\/p\u003E\u003Cp align=\u0022center\u0022\u003E\u003Cem\u003E\u0026nbsp;\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EABSTRACT:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EUtilizing the latest in technology, today\u2019s military is engaged in complex conflicts and operations across the globe.\u0026nbsp; These disparate operations can occur simultaneously and within the same theater.\u0026nbsp; Joint operational planning processes are often characterized as data, labor and time intensive, and courses of action (COA) must be planned and executed within the enemy\u2019s decision making cycle.\u0026nbsp; This represents a computational burden that scales dramatically with increasing numbers of systems and actors within the modern battlespace.\u0026nbsp; The most time consuming part of the planning process is COA analysis and wargaming.\u0026nbsp; This highlights the research objective developing a methodology to aid military planners by utilizing a new process for analyzing and evaluating COA alternatives.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EBy analyzing the effect of the interaction of the terrain, systems, and actors within the battlespace, it is hypothesized that leading indicators and metrics associated with a specific battlefield configuration will allow the identification of \u201crules of thumb\u201d to follow.\u0026nbsp; Wargaming different scenarios will result in a heterogeneous database to analyze because the variations in plan that will be simulated will have an effect on the protraction or brevity of the simulated operation.\u0026nbsp; Furthermore, this heterogeneous data is temporal in nature, where the metrics are time series associated with the occurrences on the simulated battlefield.\u0026nbsp; Using these data constraints to develop the methodology, Hierarchical Agglomerative Clustering, using the Dynamic Time Warping algorithm for similarity measurement were utilized.\u0026nbsp; Additionally, a novel method for cluster validation was created to establish relative value between different linkage algorithms using the similarity height of dendrograms and the statistical significance of the within cluster outcomes.\u0026nbsp; \u003C\/p\u003E\u003Cp\u003E\u0026nbsp; \u003C\/p\u003E\u003Cp\u003EThe primary contribution of this thesis is offering of enhanced COA analyses of wargame data allowing the identification of decision points and heuristics.\u0026nbsp; As a result of the formulation and the experiments, this work has aided in the creation of a cluster validation index that considers inner cluster similarity and the statistical significance of the outcome of scenarios.\u0026nbsp; Additionally, this work has contributed by the determination of the effect of scaling of complexity in the number of elements and in the complexity of the underlying wargame on the selection and performance of linkage algorithms, HSig Index, identification of decision points and heuristics. \u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee Members:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EProfessor Dimitri N. Mavris\u003C\/p\u003E\u003Cp\u003EProfessor Daniel P. Schrage\u003C\/p\u003E\u003Cp\u003EProfessor Graeme J. Kennedy\u003C\/p\u003E\u003Cp\u003EDr. Jean Charles Domercant\u003C\/p\u003E\u003Cp\u003EDr. K. Daniel Cooksey\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"CLUSTER ANALYSIS FOR DECISION-POINT RECOGNITION"}],"uid":"27707","created_gmt":"2016-06-27 14:46:06","changed_gmt":"2016-10-08 02:18:11","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2016-07-06T09:00:00-04:00","event_time_end":"2016-07-06T12:00:00-04:00","event_time_end_last":"2016-07-06T12:00:00-04:00","gmt_time_start":"2016-07-06 13:00:00","gmt_time_end":"2016-07-06 16:00:00","gmt_time_end_last":"2016-07-06 16:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}