ASAM ODS

DATASHEET
Title
Open Data Services
Domain
Data Management & Analysis
Current Version
6.2.0
Release Date
31 Dec 2022
Application Areas
  • Test data management for:
  • measurement data
  • fleet test data
  • simulation data
  • big-data applications
Specification Content
  • Base data model
  • Derived application data models for:
  • NVH
  • test object geometry
  • test stand calibration
  • bus data
  • testing workflows
  • Model for relational databases for the physical storage of data
  • HTTP-API using web services and data serialization via Google Protocol Buffers
  • OO-API using CORBA
  • RPC-API
  • XML file format for data exchange
  • Non-XML file format for data exchange
File Formats
  • atfx
  • atf
Standard Compliance
Cross Tests are held at irregular intervals.

ODS (Open Data Services) focuses on the persistent storage and retrieval of testing data. The standard is primarily used to set up a test data management system on top of test systems that produce measured or calculated data from testing activities. Tool components of a complex testing system can store data or retrieve data as needed for proper operation of tests or for test data post-processing and evaluation. A typical scenario for ODS in the automotive industry is the use of a central ODS server, which handles all testing data produced by vehicle test beds. The major strength of ODS as compared to non-standardized data storage solutions is that data access is independent of the IT architecture and that the data model of the database is highly adaptable yet still well-defined for different application scenarios. Despite this flexibility, clients can query the data from the database and still correctly interpret the meaning of the data. This is achieved by various means through the standard.

  • Base model: The base model is used as a parent for deriving specific application models. The base model provides a rough classification of the data in application models by adding semantics to them. This enables client tools from different vendors to correctly interpret the data.
  • Application models: Application models cover the data storage needs for specific application areas. The standard provides pre-defined application models for test object geometry, NVH testing, test stand calibration, bus data and testing workflows.
  • Format for physical storage: This part of the standard specifies how a relational database should initially be constructed to store data in a compliant way.
  • API: Clients have access to data on the ODS-server via a web-service API using the Hypertext Transfer Protocol (short: HTTP-API). Data is serialized and transferred in the Google Protocol Buffers format. For legacy reasons, the standard still contains an object-oriented API based upon the CORBA architecture (short: OO-API) and a remote procedure call API (short: RPC-API).
  • File description format: The description formats allow file-based data exchange between tools. One non-XML format (for legacy purposes) and a modern XML-format is provided.
  • External Data API (EXD-API): A programming interface which allows to access mass data stored in external files of any type as long as an EXD-API implementation for such file type is available. 

ODS servers act as data fusion centers of data from different test beds and measurement devices from different vendors. Data can be accessed independently of their origin via the same methods and interfaces. This even extends to mass-data stored in external files (ASAM MDF), where the DB only holds descriptive meta data a pointer to that external storage location. The same API methods are used for DB-internal measurement data access as well as to external data access, so that users have completely transparent access to the data. Furthermore, ODS servers are scalable, which allow to extend the data models and add more clients to the overall tool chain without having to setup a new server for every extension.

 

Standard Authors

AVL LIST GmbH, Atos IT Solutions and Services GmbH, AMS GmbH, Audi AG, Beta CAE Systems International AG, BMW AG, Canoo Engineering AG, Cologne University of Applied Science, Daimler AG, ETAS GmbH, Ford Motor Company, Gigatronic GmbH, HEAD acoustics GmbH, HORIBA Automotive Test Systems GmbH, HighQSoft GmbH, iASYS Technologies Pvt. Ltd., IPETRONIK GmbH, Karakun AG, KPIT Technologies GmbH, Kristl Seibt & Co GmbH, RA Consulting GmbH, MAN Truck & Bus AG, Müller-BBM VibroAkustik Systeme GmbH, National Instruments Corporation, NorCom Information Technology GmbH & Co. KGaA, Peak Solution GmbH, Porsche AG, Robert Bosch GmbH, Siemens AG, Volkswagen AG.


 


DATASHEET
Title
Open Data Services
Domain
Data Management & Analysis
Current Version
6.2.0
Release Date
31 Dec 2022
Application Areas
  • Test data management for:
  • measurement data
  • fleet test data
  • simulation data
  • big-data applications
Specification Content
  • Base data model
  • Derived application data models for:
  • NVH
  • test object geometry
  • test stand calibration
  • bus data
  • testing workflows
  • Model for relational databases for the physical storage of data
  • HTTP-API using web services and data serialization via Google Protocol Buffers
  • OO-API using CORBA
  • RPC-API
  • XML file format for data exchange
  • Non-XML file format for data exchange
File Formats
  • atfx
  • atf
Standard Compliance
Cross Tests are held at irregular intervals.
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