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dc.contributor.advisorLiu Sheng, Olivia R.en_US
dc.contributor.authorFang, Xiao
dc.creatorFang, Xiaoen_US
dc.date.accessioned2013-05-09T10:47:16Z
dc.date.available2013-05-09T10:47:16Z
dc.date.issued2003en_US
dc.identifier.urihttp://hdl.handle.net/10150/289930
dc.description.abstractWith the wide application of information technology in organizations, especially the rapid growth of E-Business, masses of data have been accumulated. Knowledge Discovery in Databases (KDD) gives organizations the tools to sift through vast data stores to extract knowledge supporting organizational decision making. Most of the KDD research has assumed that data is static and focused on either efficiency improvement of the KDD process (e.g., designing more efficient KDD algorithms) or business applications of KDD. However, data is dynamic in reality (i.e., new data continuously added in). Knowledge discovered using KDD becomes obsolete rapidly, as the discovered knowledge only reflects the status of its dynamic data source when running KDD. Newly added data could bring in new knowledge or invalidate some discovered knowledge. To support effective decision making, knowledge discovered using KDD needs to be updated along with its dynamic data source. In this dissertation, we research on knowledge refreshing, which we define as the process to keep knowledge discovered using KDD up-to-date with its dynamic data source. We propose an analytical model based on the theory of Markov decision process, solutions and heuristics for the knowledge refreshing problem. We also research on how to apply KDD to such application areas as intelligent web portal design and network content management. The knowledge refreshing research identifies and solves a fundamental and general problem appearing in all KDD applications; while the applied KDD research provides a test environment for solutions resulted from the knowledge refreshing research.
dc.language.isoen_USen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectBusiness Administration, Management.en_US
dc.titleKnowledge refreshing: Model, heuristics and applicationsen_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.identifier.proquest3106984en_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineBusiness Administrationen_US
thesis.degree.namePh.D.en_US
dc.identifier.bibrecord.b4464940xen_US
refterms.dateFOA2018-09-06T12:40:29Z
html.description.abstractWith the wide application of information technology in organizations, especially the rapid growth of E-Business, masses of data have been accumulated. Knowledge Discovery in Databases (KDD) gives organizations the tools to sift through vast data stores to extract knowledge supporting organizational decision making. Most of the KDD research has assumed that data is static and focused on either efficiency improvement of the KDD process (e.g., designing more efficient KDD algorithms) or business applications of KDD. However, data is dynamic in reality (i.e., new data continuously added in). Knowledge discovered using KDD becomes obsolete rapidly, as the discovered knowledge only reflects the status of its dynamic data source when running KDD. Newly added data could bring in new knowledge or invalidate some discovered knowledge. To support effective decision making, knowledge discovered using KDD needs to be updated along with its dynamic data source. In this dissertation, we research on knowledge refreshing, which we define as the process to keep knowledge discovered using KDD up-to-date with its dynamic data source. We propose an analytical model based on the theory of Markov decision process, solutions and heuristics for the knowledge refreshing problem. We also research on how to apply KDD to such application areas as intelligent web portal design and network content management. The knowledge refreshing research identifies and solves a fundamental and general problem appearing in all KDD applications; while the applied KDD research provides a test environment for solutions resulted from the knowledge refreshing research.


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