Motivation


Trueno came to fruition because of the following arguments and needs:

  • As 2017, there's not a wide variety of graphs databases available.
  • Existing solutions are either:
    • Hard to setup and deploy.
    • Propietary and closed source.
    • Not free(many with the distributed version)
    • Not scalable.
    • Does not support large range of applications.
  • The pipeline of graph analysis is slow and inefficient. This usually consist of:
    1. Parse graph data, or build graph models.
    2. Store into a graph database, in files, or not store the data at all.
    3. Write a program to analyze the graph, or use a graph processing framework such as GraphX, Giraph, and GraphLab.
    4. Store the results back to disk or get temporary results.
    5. Repeat this process with the temporary data if additional analysis is required.
  • The previous pipeline can take days or weeks to accomplish, and even more if advanced and repetitive analysis is required.
  • Building new graph algorithms and deploying them distributively is hard, time consuming, and not community friendly since solutions are scattered through repositories or not available to the public.
  • Current solutions require average to high computer science knowledge. Experts in other fields usually require additional help to deploy and analyze graphs.

What Trueno aims for?

This project was build by a group of Doctoral and Master Students at Purdue CS with the goal of accelerating graph based applications and analysis. Trueno was started taking in consideration the following characteristics to address the previous needs and arguments:

  • Easy to deploy and run, involving the least configuration possible from the user.
  • Able to handle small and large graph data.
  • Open source and free.
  • Support a broad range of applications.
  • Provide extensibility via a plugin system for new graph algorithms and a global repository.(under construction)
  • Process graph algorithms on the stored graph data.
  • Easy to integrate with other graph analysis tools such as Gephi, Gremlin, Cytoscape, etc.
  • Users should not need to learn how to code in a programming language to exploit all the features Trueno provides.

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