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Implementation of a process-based Approach for Data Collection and Conformance Checking of Testing Settings

This project is a worklist management system for the CPEE (Cloud Process Execution Engine) in the laboratory domain. It allows users to create, update, and track worklists for tests against SARS-CoV-2.

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Overview

  1. Backend Service: Encapsulates the correlator. Mainly communicates with the CPEE.
  2. Cloud Process Execution Engine (CPEE): Executes and manages the workflow.
  3. Database: MongoDB instance that stores all the data.
  4. Lab Bot: Handles notification and alerting. Can be also utilized for reporting and monitoring proposes.
  5. Frontend: User interface (GUI) for reporting and visualization of the log data.
  6. Logging Service: Receives all raw log data from various sources and stores them in the database. Notifies the backend service when a new log has been received.
  7. Reverse Proxy (NGINX): Hides backend and frontend service from being accessed directly.
  8. Process Models: Contains the lab process models for the CPEE

Installation

To install and run the project locally using Docker Compose, follow these steps:

  1. Clone the repository: $ git clone https://github.com/greschner/cpee-worklist.git
  2. Navigate to the project directory: $ cd cpee-worklist
  3. Fill out the environment variables in the .env.template file and rename it to .env.
  4. Build the Docker images: $ docker compose build
  5. Start the Docker containers: $ docker compose up

Frontend Usage

Access the Frontend at http://localhost:80

  • Generate reports and analytics to monitor lab performance
  • Search after specific well plates and samples to view their log history
  • Export log data either in CSV or JSON format
  • View open CPEE tasks which are waiting for callback

Contributing

Contributions are welcome! If you'd like to contribute to this project, please follow these guidelines:

  1. Fork the repository
  2. Create a new branch: $ git checkout -b feature/your-feature-name
  3. Make your changes and commit them: $ git commit -m "Add your feature description"
  4. Push to the branch: $ git push origin feature/your-feature-name
  5. Submit a pull request

License

This project is licensed under the MIT License. See the LICENSE file for more details.