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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Scalability Issues for Remote Sensing Infrastructure: A Case Study.

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  • 1Department of Computer Science, University of Calgary, Calgary, AB T2N 1N4, Canada. liuyang1520@gmail.com.

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Summary
This summary is machine-generated.

Researchers optimized a northern Canada sensor web for environmental data collection and sharing. They improved data collection efficiency and reduced network traffic, enhancing scientific data accessibility.

Keywords:
benchmarkingnetwork traffic measurementperformanceremote sensingscientific web sitesensor webworkload characterization

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Area of Science:

  • Environmental Science
  • Computer Science
  • Geophysics

Background:

  • A University of Calgary team operates a large sensor web for scientific data collection in northern Canada.
  • The sensor web utilizes over a thousand Internet-connected sensors, focusing on environmental data like space weather and auroral phenomena.

Purpose of the Study:

  • To evaluate the performance and scalability of a remote sensing infrastructure.
  • To identify and address bottlenecks in both data collection and data dissemination.

Main Methods:

  • Benchmarking techniques were employed to identify and resolve performance issues in memory management for TCP data streams.
  • Passive and active network traffic measurements were used to detect and minimize excessive traffic from web robots and JavaScript.

Main Results:

  • A performance bottleneck in memory management for TCP data streams was successfully fixed, improving system efficiency on multi-core architectures.
  • Excessive network traffic associated with data sharing methods was identified and reduced.

Conclusions:

  • The study provides valuable lessons learned for optimizing scientific web systems with remote sensing infrastructure.
  • Improvements in data collection and dissemination enhance the accessibility and efficiency of scientific data sharing.