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The AMS year 2018 – from MaDaM 2.2 to MDFS

As we look back on the last year our 25th anniversary was certainly a very special highlight, which we used in addition to various internal events, especially for product development. We would like to summarize all interesting technical new and further developments in an overview and give you the possibility to answer open questions. 

For our Measurement Data Management System we published a software update with numerous new features. For visual searching by clicking on the relevant bars in the graph, Haystack was developed and implemented and, in this context, the complete facet tree was revised. The Wildcard Searching has been extended with some functions. This allows users to search for content that are encapsulated, as for example *lin* in *Berlin*. Furthermore multiple importers can be controlled in a single jBEAM template. Users can define various search queries. The results can be assigned to a specific data source for the jBEAM template, whether it’s an importer, multi-file-imports or data source manager.
All new features of MaDaM 2.2.

For Big Test Data applications the Measurement-data Distributed File System (MDFS) was developed. Data files and CPU (jBEAM) are located on the same node. There are no longer restrictions in transferring data files from the NAS. In addition, the distributed file system allows grouping of related files. All files, belonging to one single test, are stored on a node. MDFS is horizontally scalable, JAVA NIO compatible and high performance.
An overview of MDFS.

Data mining functions have been integrated in our analysis tool jBEAM. On one hand they allow searching through countless data by algorithms for the recognition of patterns, coherences, clusters etc. On the other hand, they can be further developed by the user and new algorithms can be added.

The AMS offers for the evaluation of huge amount of measurement data a complete tool chain, that covers the complete workflow from data storage (MaDaM) to parallel evaluation (jBEAM Cluster) and statistical analysis of these evaluation results (Big Test Data Mining).