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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
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An analysis of the graph processing landscape.

Miguel E Coimbra1,2, Alexandre P Francisco1,2, Luís Veiga1,2

  • 1INESC-ID, R. Alves Redol 9, 1000-029 Lisbon, Portugal.

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Summary
This summary is machine-generated.

Exploring graph-based big data requires understanding network topology and metrics. This survey details computational approaches, systems, and techniques for efficient graph processing, aiding researchers and engineers.

Keywords:
Dataflow programmingDistributed systemsGraph processingGraph representationOnline processing

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Area of Science:

  • Computer Science
  • Data Science
  • Network Analysis

Background:

  • Graph-based big data offers valuable insights through network topology and metrics.
  • Computational approaches for graph data exploration are diverse and complex.

Purpose of the Study:

  • To provide a comprehensive survey of graph processing systems and techniques.
  • To familiarize readers with graph datasets, applications, and processing paradigms.

Main Methods:

  • Overview of single-machine, distributed, and high-performance computing (HPC) systems.
  • Description of system dimensions: processing paradigms, types, coordination, communication, and partitioning.
  • Discussion of techniques like load balancing and accuracy-computation trade-offs.

Main Results:

  • Categorization of graph processing systems based on defined dimensions.
  • Identification of common use-cases such as vertex ranking and community detection.
  • Analysis of techniques for performance optimization in graph computations.

Conclusions:

  • Understanding graph processing landscape is crucial for leveraging big data.
  • The survey aids experienced professionals and students in navigating graph processing solutions and limitations.