Tquery

Tquery enables querying of semi-structured ephemeral data within microservice architectures to support transient, privacy-preserving data processing at the edge and fog computing layers.

Key Features:

• Ephemeral Data Handling: Enables queries over data that do not persist beyond immediate use, supporting privacy-preserving transient data processing.
• Technology-Agnostic Framework: Implemented within the Jolie microservice programming language to integrate into heterogeneous microservice environments without imposing rigid data structures.
• Compatibility with Semi-Structured Data: Adopts operators inspired by a consistent variant of the MongoDB aggregation framework to process semi-structured data.
• Performance Optimization: Addresses throughput bottlenecks, scalability challenges, dependency management complexities, and security vulnerabilities such as query injection to improve performance compared with external databases (e.g., MongoDB) in ephemeral settings.
• Cleanroom Software Engineering: Developed via a cleanroom process that defines a theoretical querying framework for semi-structured data and preserves query-language invariants during implementation.

Scientific Applications:

• Medical algorithm implementation: Demonstrated by implementing a medical algorithm as microservices to show secure and efficient deployment using ephemeral data.
• Healthcare privacy and compliance: Enables transient processing of sensitive patient data to support privacy and regulatory compliance in healthcare contexts.
• Bioinformatics relevance: Provides a solution for ephemeral data handling in microservice architectures applicable to bioinformatics workflows.

Methodology:

Defined Tquery as a theory for querying semi-structured data aligned with Jolie and inspired by a consistent variant of MongoDB's aggregation framework operators; implemented Tquery using the Jolie type system to capture its syntax and maintain invariants.