Modeling and improvement of processes with heterogeneous sources of data

dc.contributor.advisor Paynabar, Kamran
dc.contributor.advisor Shi, Jianjun
dc.contributor.author Reisi Gahrooei, Mostafa
dc.contributor.committeeMember Gebraeel, Nagi
dc.contributor.committeeMember Mei, Yajun
dc.contributor.committeeMember Colosimo, Bianca Maria
dc.contributor.department Industrial and Systems Engineering
dc.date.accessioned 2019-08-21T13:51:31Z
dc.date.available 2019-08-21T13:51:31Z
dc.date.created 2019-08
dc.date.issued 2019-05-09
dc.date.submitted August 2019
dc.date.updated 2019-08-21T13:51:32Z
dc.description.abstract Integrating heterogeneous data in an effective manner to construct an efficient model of a system is the main theme of this thesis. Heterogeneity of data may refer to different levels of accuracy of data, different levels of information that process inputs (specifically functional inputs) may contain in explaining an output, or different forms of data. In this thesis, we will built upon the existing works and methods related to each of these classes of heterogeneity, and introduce methodologies to address existing challenges in practice.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/61709
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Heterogeneous data
dc.subject Adaptive sampling
dc.subject Functional regression
dc.subject Tensor regression
dc.title Modeling and improvement of processes with heterogeneous sources of data
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Shi, Jianjun
local.contributor.advisor Paynabar, Kamran
local.contributor.corporatename H. Milton Stewart School of Industrial and Systems Engineering
local.contributor.corporatename College of Engineering
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thesis.degree.level Doctoral
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