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Efficient execution of multiple query workloads in data analysis applications
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Source Conference on High Performance Networking and Computing archive
Proceedings of the 2001 ACM/IEEE conference on Supercomputing (CDROM) table of contents
Denver, Colorado
Pages: 53 - 53  
Year of Publication: 2001
ISBN:1-58113-293-X
Authors
Henrique Andrade  University of Maryland, College Park, MD
Tahsin Kurc  Informatics, The Ohio State University, Columbus, OH
Alan Sussman  University of Maryland, College Park, MD
Joel Saltz  Informatics, The Ohio State University, Columbus, OH
Sponsors
ACM: Association for Computing Machinery
SIGARCH: ACM Special Interest Group on Computer Architecture
IEEE-CS\DATC : IEEE Computer Society
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 6,   Downloads (12 Months): 32,   Citation Count: 4
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ABSTRACT

Applications that analyze, mine, and visualize large datasets are considered an important class of applications in many areas of science, engineering, and business. Queries commonly executed in data analysis applications often involve user-defined processing of data and application-specific data structures. If data analysis is employed in a collaborative environment, the data server should execute multiple such queries simultaneously to minimize the response time to clients. In this paper we present the design of a runtime system for executing multiple query workloads on a shared-memory machine. We describe experimental results using an application for browsing digitized microscopy images.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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A. Afework, M. D. Beynon, F. Bustamante, A. Demarzo, R. Ferreira, R. Miller, M. Silberman, J. Saltz, A. Sussman, and H. Tsang. Digital dynamic telepathology --- the Virtual Microscope. In Proceedings of the 1998 AMIA Annual Fall Symposium. American Medical Informatics Association, Nov. 1998.
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C. Chang, T. Kurc, A. Sussman, U. Catalyurek, and J. Saltz. A hypergraph-based workload partitioning strategy for parallel data aggregation. In Proceedings of the Eleventh SIAM Conference on Parallel Processing for Scientific Computing. SIAM, Mar. 2001.
 
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Collaborative Colleagues:
Henrique Andrade: colleagues
Tahsin Kurc: colleagues
Alan Sussman: colleagues
Joel Saltz: colleagues