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dc.contributor.authorGenet, Bryan Howarden_NZ
dc.date.accessioned2007-09-11T14:09:26Z
dc.date.available2007-09-26T09:09:53Z
dc.date.issued2007en_NZ
dc.identifier.citationGenet, B. H. (2007). Is Semantic Query Optimization Worthwhile? (Thesis, Doctor of Philosophy (PhD)). The University of Waikato, Hamilton, New Zealand. Retrieved from https://hdl.handle.net/10289/2531en
dc.identifier.urihttps://hdl.handle.net/10289/2531
dc.description.abstractThe term quote semantic query optimization quote (SQO) denotes a methodology whereby queries against databases are optimized using semantic information about the database objects being queried. The result of semantically optimizing a query is another query which is syntactically different to the original, but semantically equivalent and which may be answered more efficiently than the original. SQO is distinctly different from the work performed by the conventional SQL optimizer. The SQL optimizer generates a set of logically equivalent alternative execution paths based ultimately on the rules of relational algebra. However, only a small proportion of the readily available semantic information is utilised by current SQL optimizers. Researchers in SQO agree that SQO can be very effective. However, after some twenty years of research into SQO, there is still no commercial implementation. In this thesis we argue that we need to quantify the conditions for which SQO is worthwhile. We investigate what these conditions are and apply this knowledge to relational database management systems (RDBMS) with static schemas and infrequently updated data. Any semantic query optimizer requires the ability to reason using the semantic information available, in order to draw conclusions which ultimately facilitate the recasting of the original query into a form which can be answered more efficiently. This reasoning engine is currently not part of any commercial RDBMS implementation. We show how a practical semantic query optimizer may be built utilising readily available semantic information, much of it already captured by meta-data typically stored in commercial RDBMS. We develop cost models which predict an upper bound to the amount of optimization one can expect when queries are pre-processed by a semantic optimizer. We present a series of empirical results to confirm the effectiveness or otherwise of various types of SQO and demonstrate the circumstances under which SQO can be effective.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherThe University of Waikatoen_NZ
dc.rightsAll items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectsemantic query optimizationen_NZ
dc.subjectrelational databaseen_NZ
dc.subjectinterval algebraen_NZ
dc.titleIs Semantic Query Optimization Worthwhile?en_NZ
dc.typeThesisen_NZ
thesis.degree.disciplineComputing and Mathematical Sciencesen_NZ
thesis.degree.grantorUniversity of Waikatoen_NZ
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (PhD)en_NZ
uow.date.accession2007-09-11T14:09:26Zen_NZ
uow.date.available2007-09-26T09:09:53Zen_NZ
uow.identifier.adthttp://adt.waikato.ac.nz/public/adt-uow20070911.140926en_NZ
uow.date.migrated2009-06-12T04:51:42Zen_NZ
pubs.place-of-publicationHamilton, New Zealanden_NZ


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