Data Inference in Cloud Computing and Smart Grids: A Grassmann Manifold Approach

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dc.contributor.advisorLi, Zongpeng
dc.contributor.authorZhao, Yao
dc.date.accessioned2015-09-24T17:53:13Z
dc.date.available2015-11-20T08:00:40Z
dc.date.issued2015-09-24
dc.date.submitted2015en
dc.description.abstractData inference allows to infer data from a partially revealed data set or to detect corrupted data and recover the original data. In general, to infer missing data or to correct corrupted data is theoretically impossible if given no assumption about the data. Under assumptions about the intrinsic features of the data set, algorithms can be developed to recover missing or corrupted data. We consider data inference problems with a low-rank structure of the data matrix. By exploiting the low-rank feature of the matrix, the data inference problems can be modelled as an L1-norm optimization problem. We propose a framework to solve this kind of problems exploiting optimization theory on Grassmann manifold. We apply this framework to Smart Grid to detect false data injection attack and to predict the QoS of a cloud marketplace. The experiments show our framework achieves a good result under both scenarios.en_US
dc.identifier.citationZhao, Y. (2015). Data Inference in Cloud Computing and Smart Grids: A Grassmann Manifold Approach (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25519en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/25519
dc.identifier.urihttp://hdl.handle.net/11023/2488
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectComputer Science
dc.subject.classificationGrassmann manifolden_US
dc.subject.classificationData inferenceen_US
dc.subject.classificationQoSen_US
dc.subject.classificationSmart gridsen_US
dc.titleData Inference in Cloud Computing and Smart Grids: A Grassmann Manifold Approach
dc.typemaster thesis
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
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