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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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Ephemeral data handling in microservices with Tquery.

Saverio Giallorenzo1,2, Fabrizio Montesi3, Larisa Safina4

  • 1Università di Bologna, Bologna, Italy.

Peerj. Computer Science
|September 12, 2022
PubMed
Summary
This summary is machine-generated.

Jolie/Tquery is a new framework for ephemeral data querying in microservices, addressing database limitations. It offers better performance and flexibility for handling temporary data in edge and fog systems.

Keywords:
Edge computingEphemeral dataFog computingFormal methodsJolieMicroservicesQuery languagesSemi-structured dataService-oriented computinge-Health

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Area of Science:

  • Computer Science
  • Software Engineering

Background:

  • Edge and fog computing necessitate ephemeral data querying due to privacy regulations.
  • Traditional databases present challenges in microservice architectures, including bottlenecks, security risks, and data structure rigidity.

Purpose of the Study:

  • Introduce Jolie/Tquery, the first query framework for ephemeral data handling in microservices.
  • Provide a reliable and efficient solution for querying temporary data within microservice environments.

Main Methods:

  • Developed Tquery, a theory for querying semi-structured data compatible with Jolie.
  • Implemented Jolie/Tquery using a cleanroom software engineering process, leveraging the Jolie programming language and MongoDB aggregation framework principles.
  • Utilized the Jolie type system to ensure Tquery's syntax and invariants.

Main Results:

  • Jolie/Tquery effectively handles ephemeral data querying in microservices.
  • Microbenchmarks demonstrate that Jolie/Tquery outperforms external databases like MongoDB for ephemeral data scenarios.
  • The framework integrates seamlessly with microservice architectures and supports heterogeneous systems.

Conclusions:

  • Jolie/Tquery offers a robust solution for ephemeral data management in microservices.
  • The framework enhances flexibility and performance compared to traditional database approaches for temporary data.
  • It is particularly beneficial for edge and fog computing environments with strict privacy requirements.