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Real-Time Massive Vector Field Data Processing in Edge Computing.

Kun Zheng1, Kang Zheng2, Falin Fang3

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Summary
This summary is machine-generated.

Edge computing offers timely processing for massive vector field data (MVFD). A novel framework with Data Fluidization Schedule (DFS) reduces data volume and I/O latency, outperforming traditional big data systems.

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Area of Science:

  • Computer Science
  • Data Science
  • Distributed Systems

Background:

  • Real-time data processing is expanding due to sensor networks.
  • Massive Vector Field Data (MVFD) presents challenges in volume, velocity, and distribution.
  • Centralized cloud computing faces processing delays with MVFD due to data source distance.

Purpose of the Study:

  • To propose an edge computing framework for efficient MVFD processing.
  • To address high data transmission delays caused by MVFD volume.
  • To reduce Input/Output (I/O) latency in data processing.

Main Methods:

  • Developed an edge computing framework for MVFD.
  • Invented the Data Fluidization Schedule (DFS) to reduce data block volume and I/O latency.
  • Evaluated the framework using massive wind field data for cyclone recognition.

Main Results:

  • The proposed edge computing framework significantly reduces data transmission delay.
  • The Data Fluidization Schedule (DFS) effectively minimizes data block volume and I/O latency.
  • The framework demonstrated superior efficiency compared to Spark and MapReduce for MVFD processing.

Conclusions:

  • Edge computing is well-suited for timely processing of MVFD.
  • The DFS is a key innovation for overcoming MVFD volume challenges.
  • The developed framework offers a significant advancement in processing massive, distributed datasets.