Scientific datasets are often stored on distributed archival storage systems, because geographically distributed sensor devices
store the datasets in their local machines and also because the size of scientific datasets demands large amount of disk space.
Multidimensional indexing techniques have been shown to greatly improve range query performance into large scientific datasets.
In this paper, we discuss several ways of distributing a multidimensional index in order to speed up access to large distributed
scientific datasets. This paper compares the designs, challenges, and problems for distributed multidimensional indexing schemes,
and provides a comprehensive performance study of distributed indexing to provide guidelines to choose a distributed multidimensional
index for a specific data analysis application.
[Show abstract][Hide abstract] ABSTRACT: Our AES is an ideal example of a new generation of scientific analysis tools that are empowered by the rapid growth of facilities tailored for data-intensive computing. AES will greatly reduce the effort on the part of investigators to systematically search for interesting correlations and test hypotheses while also freeing researchers from the burden of managing the exploding volume of data. By supporting the ability to exchange event specifications and query results, AES greatly aids collaboration among investigators. We anticipate that AES will ultimately lead to entirely novel lines of investigation.
Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International; 01/2012
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