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 well-documented test data. Continue reading “Big Data in the field of automotive testing requires Test Data Governance”
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.
Continue reading “ASAM ODS goes Big Data”
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 Since these communication protocols are technically outdated and either badly or not supported by newer development tools, version 6.0 of ASAM ODS now additionally provides a streamlined API.
Continue reading “ASAM e.V. has released ODS 6.0”
Driving tests produce an enormous amount of measured data for different development disciplines. Some companies have therefore begun to collect these data in a so-called “Data Lake”.
At the heart of such a “Data Lake” is usually the open source platform Hadoop. It provides a variety of frameworks that enable to process and analyze the incoming data volumes flexible in manifold ways.
Continue reading “ToDo´s to extend ASAM ODS to Big Data”
Measurement data must be quickly retrievable, comparable and interpretable for different purposes and by different persons even after a long time. Companies can only achieve this by precisely documenting under which conditions (= context) the respective measurement data have been created. Conclusion 1: Transparency on test data becomes problematic, if measurement files are stored on the hard disk of a local computer or server just based on individually selected names without a detailed description.
That´s why the market offers a whole series of software tools with which this file-based storage of measurement data can be better organized.
Continue reading “Standardization and transparency in the test environment”
An important function of openMDM® 5 is the full-text search. It allows test engineers to find measurements based on arbitrary search terms and without knowing the exact location.
When choosing a suitable platform, Peak Solution investigated the two servers SolR and ElasticSearch in detail. This article gives a brief overview on how their features match with the requirements of the openMDM® 5 Eclipse Working Group and what recommendation was given.
Continue reading “openMDM® 5 Full-Text Search – Technology Proposal”
Automotive companies generate large amounts of test data from various sources such as test benches, simulations, driving and field tests. This data volume is growing exponentially day-by-day. Because traditional solutions reach their performance limits, the industry is dealing for some time with the question of how Big Data technologies can be beneficially used in the field of test data management and analysis. Frequently the companies desire to enhance established standards like ASAM ODS gradually.
The development of index-based search methods as well as new query and analysis techniques for ODS data are two concrete project examples.
Continue reading “BigODS project examples”