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DMVI: A Dynamic Materialized View Index for Efficiently Discovering Usable Views for Progressive Queries

dc.contributor.authorZhu, Chao
dc.contributor.authorZhu, Qiang
dc.contributor.authorZuzarte, Calisto
dc.contributor.authorMa, Wenbin
dc.coverage.spatialToronto, Canada
dc.date.accessioned2024-09-26T01:05:00Z
dc.date.available2024-09-26T01:05:00Z
dc.identifier.urihttps://hdl.handle.net/2027.42/195075
dc.description.abstractProgressive queries (PQ) are a new type of query emerged from numerous data intensive applications. A user formulates a PQ in several steps using a set of inter-related step-queries(SQ). Efficiently processing PQs in a DBMS is crucial in supporting these applications. In our previous work, we introduced a materialized view based approach to efficiently processing PQs, where our focus was on selection of promising materialized views. The problem of how to efficiently find usable views to answer SQs for a PQ remained open. In this paper, we present a new index technique, which is called the dynamic materialized view index (DMVI), to rapidly discover usable views to answer a given SQ. The structure of the index and the strategies to construct, maintain and use the DMVI are discussed. The Experimental results demonstrate that our technique is quite promising in improving the performance of the materialized view based query processing for PQs.
dc.subjectDatabase
dc.subjectquery processing
dc.subjectquery optimization
dc.subjectprogressive query
dc.subjectmaterialized view
dc.subjectindex
dc.titleDMVI: A Dynamic Materialized View Index for Efficiently Discovering Usable Views for Progressive Queries
dc.typeConference Paper
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/195075/2/cascon12zhu.pdf
dc.identifier.doihttps://dx.doi.org/10.7302/24314
dc.date.updated2024-09-26T01:04:59Z
dc.description.filedescriptionDescription of cascon12zhu.pdf : Published version
dc.identifier.name-orcidZhu, Chao
dc.identifier.name-orcidZhu, Qiang
dc.identifier.name-orcidZuzarte, Calisto
dc.identifier.name-orcidMa, Wenbin
dc.working.doi10.7302/24314en
dc.owningcollnameComputer and Information Science, Department of (UM-Dearborn)


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