Big Data in the field of automotive testing requires Test Data Governance

Big Data in the field of automotive testing is mainly about the analysis of huge amounts of data that result from a wide range of test benches and driving tests. The aim is to identify new patterns and correlations in these test data in order to describe a complex system of different components and to predict the future behavior of this system. In this way, e.g. it is possible to gain important insights for the development of new simulation models and to achieve considerable savings in the execution of cost-intensive tests. Among other things, the basis for this are tools for Data Mining, Predictive Analytics or Machine Learning, which are nowadays available in a large number on the market – in some cases even free of charge.

However, the use of these tools requires that well-documented test data is available. In practice, this is often the point at which the problem begins: On the one hand, many test engineers do not consider the maintenance of meta information as “sexy”. On the other hand, extensive knowledge of the test context is crucial for interpreting measurements correctly in the long term and independently of individual persons. If the systematic documentation of the test context is missing, the measured data is only a column of figures that lose value very quickly.

Up to now, many test departments neglect metadata management – if one can speak of it at all. In particular, its embedding in the test processes is at best deficient. Guidelines for dealing with metadata, which prescribe uniform processes and responsibilities for data entry and maintenance (= Test Data Governance), are often looked for in vain. In addition, many of the systems used for measurement data management (MDM) do not offer sufficient functionalities to support and enforce such Test Data Governance. Unfortunately, the companies still give not enough attention to this fact during the planning and selection of MDM platforms.

This results in numerous, often department-specific and isolated solutions with individual and mostly rudimentary documented test data stocks. These are only little suitable as a basis for the implementation of Big Data analyzes deployed across many domains.

Conclusion: Test departments that want to tap the potential of Big Data in the near future are doing well to deal with the topic of Test Data Governance today. Standards such as ASAM ODS provide the appropriate basis for this. Software platforms, such as openMDM or Peak Resource Planner, help to anchor the topic firmly in the company-specific test processes.

Peak Solution at Automotive Testing Expo Stuttgart 2017

Cross-site utilization of test resources, integrated test request and order processes, global access to test data and big (test) data solutions: These are the topics that are the focus of Peak Solution at Automotive Testing Expo Europe 2017 in Stuttgart (20th to 22nd of June).

We present there:

  • The web-based version of Peak Resource Planner, which, in addition to the conflict-free planning of test tasks, provides e.g. functions for the standardized description of test requirements, the control of work tasks and the documentation of the related test information.
  • An add-on for test planning that automatically suggests test plans based on customizable algorithms. Depending on the objective, this leads to a significantly higher utilization of existing test resources or shorter throughput times.
  • Solutions for processing test data using big data methods, in order to gain new insights for vehicle development.
  • The latest version of openMDM® 5: The web-based architecture of the well-known measurement data management framework supports the connection of multiple, globally distributed data sources and enables the implementation of cloud solutions for measurement data management based on the industry standard ASAM ODS.

We are looking forward to stimulating discussions with our visitors!

Peak Solution and PSA Group collaborate for Big Data Solution

Peak Solution and PSA Groupe have agreed to collaborate on a ‘Big Data’ project. The aim is to develop a flexible and scalable solution to systematically manage and analyze the huge amount of data that results from car testing. The two partners will also incorporate into the project recommendations and results from the ASAM-BigODS Work Group.

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ASAM ODS goes Big Data

The ASAM e.V. is currently working on a project that prepares the expansion of ASAM ODS to Big Data. Phase 1 of the project was finished successfully last year with a collection of use-cases, features and non-functional requirements of end users for processing large amounts of data throughout the automotive development process. Now, ASAM Technical Steering Committee (TSC) has approved phase 2 of the project.

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ASAM e.V. has released ODS 6.0

The ASAM ODS base standard in version 5.x offers clients access to ODS servers by means of CORBA and RPC. Since these communication protocols are technically outdated and either badly or not supported by newer development tools, version 6.0 of ASAM ODS now additionally provides a streamlined API.

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Enhancement of the openMDM® 5 Web Client

The openMDM® EWG (Eclipse Working Group) published a web-based prototype client for navigating and searching in test data in the middle of 2016. In December 2016, Peak Solution began to expand this client on behalf of BMW. The goal is to prepare openMDM® 5 for productive use at BMW Group.

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How to document tests in a standardized way

In order to be able to find measurement data quickly even after a long time and to interpret and compare it unambiguously for different purposes, it is necessary to precisely document the context in which they were created. With the help of templates, this can be standardized across team and department boundaries.

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