Arie Shoshani

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Bitmap indices are efficient for answering queries on low-cardinality attributes. In this article, we present a new compression scheme called <i>Word-Aligned Hybrid</i> (WAH) code that makes compressed bitmap indices efficient even for high-cardinality attributes. We further prove that the new compressed bitmap index, like the best variants of the B-tree(More)
It is well established that bitmap indices are efficient for read-only attributes with low attribute cardinalities. For an attribute with a high cardinality, the size of the bitmap index can be very large. To overcome this size problem, specialized compression schemes are used. Even though there are empirical evidences that some of these compression schemes(More)
Understanding the Earth's climate system and how it might be changing is a preeminent scientific challenge. Global climate models are used to simulate past, present, and future climates, and experiments are executed continuously on an array of distributed supercomputers. The resulting data archive, spread over several sites, currently contains upwards of(More)
The amount of scientific data generated by simulations or collected from large scale experiments have reached levels that cannot be stored in the researcher’s workstation or even in his/her local computer center. Such data are vital to large scientific collaborations dispersed over wide-area networks. In the past, the concept of a Grid infrastructure [1](More)
In numerous scientific disciplines, terabyte and soon petabyte-scale data collections are emerging as critical community resources. A new class of Data Grid infrastructure is required to support management, transport, distributed access to, and analysis of these datasets by potentially thousands of users. Researchers who face this challenge include the(More)