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Abstract: Applications that make use of very large scientific datasets have become an increasingly important subset of scientific applications. In these applications, the datasets are often multi-dimensional, i.e., data items are associated with points in a multi-dimensional attribute space. The processing is usually highly stylized, with the basic processing steps consisting of (1) retrieval of a subset of all available data in the input dataset via a range query, (2) projection of each input data item... (Update)
Context of citations to this paper: More
.... best to partition the workload for execution of queries, taking into consideration various memory requirements and application characteristics [5]. Query planning effectively breaks query execution into steps so that each step can be executed without paging. At the end of ....
...on a parallel machine. While a number of applications have been developed using ADR and high performance has been demonstrated [8, 9, 10, 11, 47], developing applications in this style requires detailed knowledge of the design of the system. In comparison, our proposed...
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BibTeX entry: (Update)
C. Chang, T. Kurc, A. Sussman, and J. Saltz. Query planning for range queries with user-defined aggregation on multi-dimensional scientific datasets. Technical Report CS-TR-3996 and UMIACS-TR-99-15, University of Maryland, Department of Computer Science and UMIACS, Feb. 1999. http://citeseer.ist.psu.edu/chang99query.html More
@techreport{ chang99query,
author = "Chialin Chang and Tahsin Kurc and Alan Sussman and Joel Saltz",
title = "Query Planning for Range Queries with User-defined Aggregation on Multi-dimensional Scientific Datasets",
number = "CS-TR-3996",
year = "1999",
url = "citeseer.ist.psu.edu/chang99query.html" }
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