Querying Very Large Multi-dimensional Datasets in ADR - Extended Abstract

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dc.contributor.authorKurc, Tahsinen_US
dc.contributor.authorChang, Chialinen_US
dc.contributor.authorFerreira, Renatoen_US
dc.contributor.authorSussman, Alanen_US
dc.contributor.authorSaltz, Joelen_US
dc.date.accessioned2004-05-31T22:57:36Z
dc.date.available2004-05-31T22:57:36Z
dc.date.created1999-05en_US
dc.date.issued1999-05-26en_US
dc.description.abstractThis paper addresses optimizing the execution of range queries into multi-dimensional datasets on distributed memory parallel machines within the Active Data Repository framework. ADR is an infrastructure that integrates storage, retrieval and processing of large multi-dimensional datasets on distributed memory parallel architectures with multiple disks attached to each node. We describe three potential strategies for efficient execution of such queries that employ different tiling and workload partitioning approaches. We evaluate scalability of these strategies for different application scenarios, varying both the number of processors and the input dataset size on a 128 processor IBM SP multicomputer. Also cross-referenced as UMIACS-TR-99-29en_US
dc.format.extent415045 bytes
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/1903/1011
dc.language.isoen_US
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_US
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_US
dc.relation.isAvailableAtTech Reports in Computer Science and Engineeringen_US
dc.relation.isAvailableAtUMIACS Technical Reportsen_US
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-4022en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-99-29en_US
dc.titleQuerying Very Large Multi-dimensional Datasets in ADR - Extended Abstracten_US
dc.typeTechnical Reporten_US

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