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Towards Scalable Graph Computation on Mobile Devices.

Yiqi Chen1, Zhiyuan Lin1, Robert Pienta1

  • 1College of Computing, Georgia Tech, Atlanta, GA, USA.

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|April 11, 2015
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Summary
This summary is machine-generated.

This study introduces a novel method for mobile devices to perform large graph computations on-device. This approach enables scalable data analysis without cloud reliance, using memory mapping for efficiency on iOS and Android.

Keywords:
graph miningmemory mappingmobile devicescalable algorithms

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Area of Science:

  • Computer Science
  • Mobile Computing
  • Graph Theory

Background:

  • Mobile devices offer portability and computational power, driving interest in on-device computation.
  • Existing methods often rely on cloud infrastructure for large-scale data analysis.

Purpose of the Study:

  • To develop a novel approach for scalable graph computation on a single mobile device.
  • To enable on-device analysis of large datasets that exceed device memory limitations.
  • To avoid reliance on cloud services for complex computations.

Main Methods:

  • Leveraging the memory mapping capability of mobile operating systems (iOS and Android).
  • Designing a simple yet powerful approach for scalable graph processing.
  • Implementing and testing the technique on a mobile device with a large real-world graph.

Main Results:

  • Demonstrated fast computation on a graph with 272 million edges (Google+ social graph) using an iPad mini.
  • Achieved computational speeds comparable to a Macbook Pro, only a few times slower.
  • Successfully created a real-world iOS application showcasing the technique's potential.

Conclusions:

  • The proposed memory mapping approach enables scalable graph computation on single mobile devices.
  • This technique significantly expands the possibilities for on-device data analysis.
  • The method shows strong potential for practical applications across mobile platforms.