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CISE Reasearch Instrumentation: A Data-Intensive DistributedComputing System

CISE Reasearch Instrumentation: A Data-Intensive DistributedComputing System
CISE 研究仪器:数据密集型分布式计算系统
批准号:
9617465
负责人:
Jon Solworth
金额:
$12.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
1997
资助国家:
美国
项目状态:
已结题
起止时间:
1997-02-15 至 1999-01-31

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英文摘要
9617465 Solworth, Jon A. Lobo, Jorge University of Illinois at Chicago CISE Research Instrumentation: A Data-Intensive Distributed Computing System As ever larger data sets--both structured and unstructured--are created and digitized, new techniques are need to better store, organize, and access this data. This is challenging since mass storage subsystem performance has increased slowly relative to processor performance and because data is usually distributed over computers which communicate using high-speed networks. Hence, new storage optimization techniques are needed for both performance and availability. This research instrumentation grant facilitates acquiring a system consisting of workstations with very large experimental disk and tape capacity, communicating over very high speed networks to empowered research in the following projects: -Persistent Object Store System- Dynamically Distributed Replication Experiments-Picture and Video Retrieval by Contents System-Distributed Database Processing System Experimental evaluation of realistic data-intensive applications and systems will thus be enabled. The equipment will be used to construct a picture and video retrieval by contents application enabling large video archives to be queried by characteristics. A distributed deductive database system targeted initially at answering protein data bank queries enables rule-based data analysis. These applications exist in prototype form and will now be scaled to realistic sized systems, enabling better understanding of performance and semantics. Complementing these applications is system research on constructing large scale object stores and distributed replication of data. This systems research would enable the construction of higher performance, more flexible stores while easing the programming burden.
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