Making existing knowledge transparent with Big Data search engines

Test and simulation departments of vehicle manufacturers and suppliers generate daily large amounts of data. This data provides valuable knowledge, which contributes significantly to the product quality and thus improve competitiveness. However, it is becoming increasingly difficult for companies to manage this knowledge across the enterprise and use it selectively. The reason: test and simulation results are stored in many different applications at various locations all over the world and often can not or only with great effort be found and linked. Manual searches are becoming a kind of “time thief” and usually do not provide a complete overview of the available information. Knowledge that could provide the decisive advantage over competitors can not be used.

However, there are solutions available that make it possible to manage and exploratory browse large amounts of data from the test and simulation environment. In addition to established standards such as ASAM ODS and openMDM® this includes increasingly Big Data technologies.

For example, search engines pursue the target to understand existing information and provide it to users across the entire enterprise in the right context as part of their privileges. For this purpose they integrate diverse data sources in the search process and execute it intelligent and semantically. In this way, employees do not any longer have to search in file systems and proprietary applications individually to find the information they need.

A full-text search based on metadata (e.g. unit under test, test equipment, load case, etc.) has the ability to detect misspellings and provide alternatives by means of “did you mean” functions. In addition, the solution recognizes synonyms and consider if necessary background information about the user (for example his field of activity) within the search.

In advanced search engines, also the analysis of measurement data, test results and simulation models may be part of a search request. In this way, it is possible to detect complex relationships between information and response questions such as “Which tests show an oil temperature increase of more than 40 K within 10 s” efficiently. This not only saves time, but is also the basis to shorten and optimize business processes.

Against this background, enterprise search engines for test and simulation data have become a top issue on the agenda of leading vehicle manufacturers and suppliers. Through them, test engineers get an instant and comprehensive overview of all the information relevant for their work.

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