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A knowledge-based approach to multiple query processing

dc.contributor.authorPark, J. T.en_US
dc.contributor.authorTeorey, Toby J.en_US
dc.contributor.authorLafortune, Stéphaneen_US
dc.date.accessioned2006-04-07T20:54:16Z
dc.date.available2006-04-07T20:54:16Z
dc.date.issued1989-02en_US
dc.identifier.citationPark, J. T., Teorey, T. J., Lafortune, S. (1989/02)."A knowledge-based approach to multiple query processing." Data &amp; Knowledge Engineering 3(4): 261-284. <http://hdl.handle.net/2027.42/28071>en_US
dc.identifier.urihttp://www.sciencedirect.com/science/article/B6TYX-4808W44-18/2/4bf600a570bf93d6412279927f2480a9en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/28071
dc.description.abstractThe collective processing of multiple queries in a database system has recently received renewed attention due to its capability of improving the overall performance of a database system and its applicability to the design of knowledge-based expert systems and extensible database systems. A new multiple query processing strategy is presented which utilizes semantic knowledge on data integrity and information on predicate conditions of the access paths (plans) of queries. The processing of multiple queries is accomplished by the utilization of subset relationships between intermediate results of query executions, which are inferred employing both semantic and logical information. Given a set of fixed order access plans, the A* algorithm is used to find the set of reformulated access plans which is optimal for a given collection of semantic knowledge.en_US
dc.format.extent1940358 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherElsevieren_US
dc.titleA knowledge-based approach to multiple query processingen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelPhilosophyen_US
dc.subject.hlbsecondlevelComputer Scienceen_US
dc.subject.hlbtoplevelHumanitiesen_US
dc.subject.hlbtoplevelEngineeringen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumComputing Research Laboratory, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109-2122, U.S.A.en_US
dc.contributor.affiliationumComputing Research Laboratory, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109-2122, U.S.A.en_US
dc.contributor.affiliationumComputing Research Laboratory, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109-2122, U.S.A.en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/28071/1/0000514.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1016/0169-023X(89)90013-Xen_US
dc.identifier.sourceData &amp; Knowledge Engineeringen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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