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dc.contributor.advisorLudwig, Simone A.en_US
dc.creatorNaseri, Mahsaen_US
dc.date.accessioned2014-03-04T12:00:14Z
dc.date.available2014-03-04T12:00:14Z
dc.date.created2013-12en_US
dc.date.issued2014-03-03en_US
dc.date.submittedDecember 2013en_US
dc.identifier.urihttp://hdl.handle.net/10388/ETD-2013-12-1419en_US
dc.description.abstractIn service-oriented environments, services are put together in the form of a workflow with the aim of distributed problem solving. Capturing the execution details of the services' transformations is a significant advantage of using workflows. These execution details, referred to as provenance information, are usually traced automatically and stored in provenance stores. Provenance data contains the data recorded by a workflow engine during a workflow execution. It identifies what data is passed between services, which services are involved, and how results are eventually generated for particular sets of input values. Provenance information is of great importance and has found its way through areas in computer science such as: Bioinformatics, database, social, sensor networks, etc. Current exploitation and application of provenance data is very limited as provenance systems started being developed for specific applications. Thus, applying learning and knowledge discovery methods to provenance data can provide rich and useful information on workflows and services. Therefore, in this work, the challenges with workflows and services are studied to discover the possibilities and benefits of providing solutions by using provenance data. A multifunctional architecture is presented which addresses the workflow and service issues by exploiting provenance data. These challenges include workflow composition, abstract workflow selection, refinement, evaluation, and graph model extraction. The specific contribution of the proposed architecture is its novelty in providing a basis for taking advantage of the previous execution details of services and workflows along with artificial intelligence and knowledge management techniques to resolve the major challenges regarding workflows. The presented architecture is application-independent and could be deployed in any area. The requirements for such an architecture along with its building components are discussed. Furthermore, the responsibility of the components, related works and the implementation details of the architecture along with each component are presented.en_US
dc.language.isoengen_US
dc.subjectWorkflow, Provenance, Worflow Evaluation, Service Composition, Service Selection, Hidden Markov Model, Partially Observable Markov Decision Process, Bayesian Structure Learningen_US
dc.titleA MULTI-FUNCTIONAL PROVENANCE ARCHITECTURE: CHALLENGES AND SOLUTIONSen_US
thesis.degree.departmentComputer Scienceen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorUniversity of Saskatchewanen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophy (Ph.D.)en_US
dc.type.materialtexten_US
dc.type.genreThesisen_US
dc.contributor.committeeMemberOsgood, Nathanielen_US
dc.contributor.committeeMemberHorsch, Michaelen_US
dc.contributor.committeeMemberMcCalla, Gorden_US
dc.contributor.committeeMemberLi, Longhaien_US


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