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Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit.

Ben Blamey1, Salman Toor1, Martin Dahlö2,3

  • 1Department of Information Technology, Uppsala University, Lägerhyddsvägen 2, 75237 Uppsala, Sweden.

Gigascience
|March 19, 2021
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Summary

Life science data streams are resource-intensive. The HASTE Toolkit uses a pipeline model to create a data hierarchy, optimizing storage, compute, and network resources for efficient scientific experiments.

Keywords:
HASTEimage analysisinterestingness functionsstream processingtiered storage

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Scientific Data Management

Background:

  • Life science applications generate large, resource-intensive data streams.
  • Processing, transporting, and storing these datasets pose significant challenges.
  • A novel pipeline model is proposed to manage these data streams effectively.

Purpose of the Study:

  • To introduce a pipeline model for organizing scientific data streams into a tiered data hierarchy.
  • To present the HASTE Toolkit, a cloud-native software toolkit implementing this model.
  • To optimize the use of limited computing resources for data-intensive life science applications.

Main Methods:

  • An "interestingness function" scores data objects, creating a data hierarchy.
  • A "policy" guides computational resource allocation based on data object scores.
  • The HASTE Toolkit provides tools to implement this prioritization strategy.
  • Evaluation involved two microscopy imaging case studies: high content screening and transmission electron microscopy edge processing.

Main Results:

  • Developed smart data pipelines that efficiently utilize storage, compute, and network resources.
  • Demonstrated effective prioritization of data streams in both on-premise and cloud environments.
  • Achieved more efficient data-intensive experiments through optimized resource allocation.

Conclusions:

  • The pipeline model and HASTE Toolkit enable efficient resource management for large-scale scientific data.
  • A clear separation between data prioritization logic and resource implementation is beneficial.
  • The toolkit offers a flexible solution for intelligent data prioritization in diverse deployment scenarios.