About & history
DataCROP™ is a data-collection framework designed for configurability, extensibility, dynamic setup and stream handling. It is detached from the underlying infrastructure through a specialized data model for data sources, processors and results, which makes solutions discoverable, interoperable and configurable. It has been applied in IoT settings across domains such as industrial maintenance and cybersecurity — for example collecting security-related data from monitored IoT systems and detecting abnormal behaviour with simple filtering or AI algorithms.
Versions
| Version | Origin | Technologies | Maturity |
|---|---|---|---|
| Barley (v1.0) | FAR-EDGE (2016–2019) | MongoDB, Apache Kafka, RabbitMQ, Kafka Streams, Node.js, React, optional Hyperledger Fabric | TRL 6 |
| Farro (v2.0) | PROPHESY (2017–2020), demonstrated in QU4LITY (2019–2022) | MongoDB, Kafka, RabbitMQ, Node.js, React; Java (Qarma), Python (RUL, health index) and R algorithms | TRL 6 |
| Maize (v3.0) | SecureIoT (2018–2021), IoTAC (2020–2023), STAR (2020–2023) and follow-up projects | Workflow Management Engine: Vue 3, Spring Boot, MongoDB, Keycloak, Apache Airflow (Celery), Docker, Kafka, Elastic stack | Active development |
A deployable Farro demo is available at farro-demo-deployment-scripts. Maize is deployed with maize-mvp.
Get in touch
- DataCROP forum
- Email the team — questions and issue reports
- GitHub · Docker Hub · OpenHUB stats · Website