BaSyx DataBridge Configurator workflow diagram

Digital Twins

Automated Configuration of BaSyx DataBridge using Standardized Asset Administration Shell Interface Modelling

How model-driven automation transforms BaSyx DataBridge configuration from hours of manual JSON editing to seconds of automated generation using standardized AAS interface modelling.

Author: Andrés Pérez
Date: 22/09/2025 10:00

Contemporary industrial systems face significant challenges when attempting to integrate diverse protocols and heterogeneous data sources. While digital twin technologies and data integration frameworks promise seamless interoperability, the reality often involves tedious manual configuration processes—particularly when connecting raw sensor data to standardized digital representations.

The Asset Administration Shell (AAS), a core component of the RAMI 4.0 architecture, establishes a unified approach for modeling, synchronizing, and enabling interoperability between physical assets through their digital counterparts. Eclipse BaSyx DataBridge serves as a critical integration layer, facilitating data flow between various sources and AAS SubmodelElements through multiple communication protocols including REST APIs, Kafka messaging, MQTT, and OPC UA.

However, conventional DataBridge implementation presents scalability challenges. Each data point requires multiple configuration files—transformation logic, protocol specifications, server endpoints, and routing definitions. While manageable for small-scale deployments, this approach becomes increasingly complex and error-prone when dealing with hundreds of variables and frequent structural modifications.

Transitioning to Automated Configuration Management

Our experience digitalizing the XXL Pilot Factory within the CONVERGING project highlighted these limitations when integrating approximately 300 data points into the BaSyx AAS infrastructure. Iterative development cycles required constant configuration updates, demonstrating the inefficiency of manual approaches.

The solution emerged from treating the DataBridge configuration itself as an Asset Administration Shell—creating a machine-readable, standards-compliant model capable of automatically generating all necessary configuration artifacts.

Introducing the Automated Configuration Framework

Our team developed the DataBridge Configurator, a Python-based automation tool that interprets specialized "DataBridge AAS" models to instantly generate complete BaSyx DataBridge configurations.

The framework leverages three essential submodel components:

Communication Message Model (custom implementation): Maps raw data structures from source systems (including objects, arrays, and properties) to corresponding AAS elements (SubmodelElementCollections, SubmodelElementLists, Properties).
AID – Asset Interface Description (IDTA standard): Provides standardized protocol definitions (Kafka, HTTP, etc.), endpoint specifications, and interaction metadata including topics, paths, and content types.
AIMC – Asset Interface Mapping Configuration (IDTA standard): Establishes standardized mappings connecting data sources through message structures to HTTP/AAS endpoints.

This integrated approach defines comprehensive data flow patterns from source systems to AAS destinations. The Configurator processes these specifications to generate all required DataBridge components including consumers, transformers, server configurations, and routing tables.

Generated Configuration Artifacts:

consumer.json - Messaging consumer definitions (Kafka topic subscriptions, consumer groups, connection parameters)
aasserver.json - AAS server endpoint configurations and SubmodelElement path mappings
JSONata transformation files - Individual data transformation rules (generated per data point)
jsondatatransformer.json - Master transformation registry and processing pipeline definitions
routes.json - Complete data flow orchestration connecting sources, transformations, and destinations

The automation delivers remarkable efficiency gains: configuration time reduces from hours to mere seconds; data mappings become consistent and auditable; any modifications to the AAS model trigger immediate regeneration of the entire configuration stack.

Complete implementation available at: https://github.com/aimenct/basyx-databridge-configurator

Industrial Implementation and Results

The AIMEN Technology Centre's XXL Pilot Factory serves as our primary validation environment, where industrial robots (ABB systems), human operator monitoring systems, and ergonomic assessment tools continuously stream operational data through Kafka messaging infrastructure. The DataBridge framework ensures seamless synchronization of this telemetry with the central AAS Server.

Implementation outcomes using our automated configurator:

Automated configuration of approximately 280 data variables across multiple systems
Seamless generation of individual JSONata transformations, AAS endpoint mappings, and data routing specifications
Streamlined development workflow: modify AAS model specifications, execute regeneration, deploy updated configuration

Strategic Advantages

Efficiency transformation: eliminates manual configuration overhead, reducing setup time from hours to instantaneous generation.
Enhanced reliability: establishes single source of truth through AAS modeling; eliminates configuration inconsistencies through machine-readable specifications.
Development agility: structural modifications in data schemas or target systems trigger seamless configuration regeneration.
Standards compliance: built upon IDTA AID/AIMC specifications and maintains alignment with IEC 63278-1 standards.

Future Development Roadmap

Open source contribution: integrate configurator capabilities into the official Eclipse BaSyx DataBridge ecosystem.
Runtime configuration management: transition from static file-based approaches to dynamic in-memory configuration updates—enabling live route and transformation modifications without system restarts.
Standards evolution tracking: maintain compatibility with evolving AID/AIMC specifications while proposing Communication Message submodel standardization.
Enhanced automation capabilities: implement automatic Communication Message submodel generation from JSON schemas and derive HTTP interface definitions directly from asset AAS models.
User experience enhancement: develop specialized graphical interfaces for rapid AIMC submodel creation and management.

Conference Presentation - ETFA 2025

This research contribution "Automated Configuration of BaSyx DataBridge using Standardized Asset Administration Shell Interface Modelling" was showcased at the ETFA 2025 conference in Porto, demonstrating AIMEN Technology Centre's innovative methodology for scalable, automated digital twin integration solutions.

Research Team

Andrés Pérez | Davinia Fernández | Emilio Costa | Lucía Alonso