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dc.contributor.advisorKamel, Magdi
dc.contributor.authorStafford, William B.
dc.date.accessioned2012-03-14T17:43:28Z
dc.date.available2012-03-14T17:43:28Z
dc.date.issued2009-03
dc.identifier.urihttp://hdl.handle.net/10945/4896
dc.descriptionApproved for public release, distribution unlimiteden_US
dc.description.abstractImprovised explosive device (IED) attacks are a significant threat to coalition forces. Defeating IEDs as weapons of strategic influence has become a major objective of Combatant Commanders and their respective Joint Task Forces. This thesis attempts to identify new approaches that can help operational forces mitigate the risk of IED attacks by identifying common sequences of events that occur before an IED attack and forecasting the number of attacks in the immediate future. Using the CARMA association rules algorithm on historical data of religious, political, and IED attack events, a model is developed to explore commonly occurring sequences of events leading to an insurgency IED attack and to predict events that are likely to occur given the sequence observed to date. Time series models are also generated to identify trends and relationships that can be helpful in forecasting future monthly IED attacks based upon previous actual historical attacks. The identified sequences and forecasts could be used to help plan troop movements, rotations, force levels, as well as allocating limited resources to address imminent threats.en_US
dc.format.extentxiv, 79 p. : ill., 1 col. map ;en_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.subject.lcshRisken_US
dc.titleSequential pattern detection and time series models for predicting IED attacksen_US
dc.typeThesisen_US
dc.contributor.corporateNaval Postgraduate School (U.S.)
dc.description.serviceUS Navy (USN) author.en_US
dc.identifier.oclc319439385
etd.thesisdegree.nameM.S.en_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineInformation Technology Managementen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
etd.verifiednoen_US


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