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Enabling big geoscience data analytics with a cloud-based, MapReduce-enabled and service-oriented workflow framework.

Zhenlong Li1, Chaowei Yang1, Baoxuan Jin2

  • 1NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, United States of America.

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Summary
This summary is machine-generated.

Geoscientists can now analyze massive datasets more efficiently using a new scientific workflow framework. This approach simplifies complex data analytics, reducing processing time for big geoscience data.

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Area of Science:

  • Geoscience
  • Data Science
  • Cloud Computing

Background:

  • Geoscience research generates vast, multi-dimensional datasets.
  • Analyzing big geoscience data is computationally intensive and requires multiple tools, posing challenges for researchers.

Purpose of the Study:

  • To propose a scientific workflow framework for efficient big geoscience data analytics.
  • To simplify complex data analysis procedures for geoscientists.

Main Methods:

  • Leveraging cloud computing, MapReduce, and Service Oriented Architecture (SOA).
  • Utilizing HBase for distributed data storage and management.
  • Developing a MapReduce-based algorithm framework for parallel data processing.
  • Building a service-oriented workflow architecture for on-demand cloud analytics.

Main Results:

  • A proof-of-concept prototype demonstrated the framework's effectiveness.
  • Significant improvements in the efficiency of big geoscience data analytics were observed.
  • Reduced data processing times and simplified analytical procedures for geoscientists.

Conclusions:

  • The proposed framework effectively addresses the challenges of big geoscience data analytics.
  • It enhances efficiency and accessibility for geoscientists, enabling more complex analyses.