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dc.contributor.authorNimmagadda, Shastri Lakshman
dc.contributor.supervisorProf Heinz Dreher
dc.contributor.supervisorDr Douglas Atkinson
dc.contributor.supervisorDr Ananda Jeeva
dc.date.accessioned2017-01-30T10:20:25Z
dc.date.available2017-01-30T10:20:25Z
dc.date.created2015-06-23T01:57:34Z
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/20.500.11937/2322
dc.description.abstract

Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared in more than 50 international conference proceedings and journals.

dc.languageen
dc.publisherCurtin University
dc.titleOntology based data warehousing for mining of heterogeneous and multidimensional data sources
dc.typeThesis
dcterms.educationLevelPhD
curtin.departmentSchool of Information Systems, Curtin Business School
curtin.accessStatusOpen access


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