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Improving Access to Multi-dimensional Self-describing Scientific Datasets

dc.contributor.authorNam, Beomseoken_US
dc.contributor.authorSussman, Alanen_US
dc.description.abstractApplications that query into very large multi-dimensional datasets are becoming more common. Many self-describing scientific data file formats have also emerged, which have structural metadata to help navigate the multi-dimensional arrays that are stored in the files. The files may also contain application-specific semantic metadata. In this paper, we discuss efficient methods for performing searches for subsets of multi-dimensional data objects, using semantic information to build multi-dimensional indexes, and group data items into properly sized chunks to maximize disk I/O bandwidth. This work is the first step in the design and implementation of a generic indexing library that will work with various high-dimension scientific data file formats containing semantic information about the stored data. To validate the approach, we have implemented indexing structures for NASA remote sensing data stored in the HDF format with a specific schema (HDF-EOS), and show the performance improvements that are gained from indexing the datasets, compared to using the existing HDF library for accessing the data. (UMIACS-TR-2002-99)en_US
dc.format.extent285369 bytes
dc.format.extent86422 bytes
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-4419en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-2002-99en_US
dc.titleImproving Access to Multi-dimensional Self-describing Scientific Datasetsen_US
dc.typeTechnical Reporten_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

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