When analyzing vast amount of data, one term inevitably becomes relevant: Data Mining. By applying statistical methods, countless data points are processed by algorithms for the recognition of patterns, coherences, and clusters amongst other things. The AMS offers a tool-chain that covers the complete workflow necessary for analyzing Big Data – from data storage with MaDaM to parallel evaluation with jBEAM-Cluster and even including statistical analysis of these evaluation results (Test Data Mining).
The AMS BIG-TEST-DATA approach offers multiple technologies for distributed analysis from one
provider. There are three core products used to obtain the maximum information from test and
measurement data for engineers: 1. jBEAM: The analysis and visualization tool, Java based, useful for
both server and desktop application, with 18 years of experience and continuous development. 2.
MaDaM: The Measurement Data Management system (MDM), available now as version 2 with
Elasticsearch indexing technology and modern web interfaces. 3. jBEAM-Cluster: Cluster management
software for parallel and distributed analysis of a myriad of data files – an essential and time-saving
In the field of measurement data, the data volumes continue to increase exponentially. Without an
organizational tool like MaDaM, no intelligent post-processing can be done. The subsequent data
mining process also has its starting point in this central MDM system. The MaDaM importer selects
information about each measured channel and analyzes it to extract statistical information besides the
regular metadata. Data about the related engineer and test object as well as such statistical values are
indexed and made available for later search queries.
The new version 2 of MaDaM was completely redesigned based on the 4 years of experience with
version 1. Elasticsearch replaces the former Lucene technology and offers horizontal scalability without
and short response times. Typically, the data mining process based on individual single measurement values starts with selecting a set of relevant tests. Relevant are all tests that can be used to answer the questions that are looked for. The number of such tests can be in the hundreds or in the hundreds of thousands. All of these files together with the definition of the analysis that should be performed is provided to the jBEAM-Cluster as a job. The files are analyzed in parallel and close to the storage location while taking into account each individual value of each channel. The statistical results of each file are aggregated and sent back to the originator of the job.
The final step is analyzing these results with data mining methodologies: jBEAM has a whole set of data mining algorithms, covering the approaches Clustering, Pattern Recognition, Prediction, and Reducing Dimension of Relation amongst others. After this data mining step, the calculated result can be visualized with dedicated graphs as a report. Each intermediate step can be controlled manually where the operator can optimize the boundary conditions of the data mining process in a highly interactive way. These three steps focused on and optimized for measurement data are the essentials for the Test Data Mining solution of AMS.
Analyse, Visualisierung & Management von Messdaten
Unser Ziel ist es, mit zukunftsträchtigen Softwarelösungen Ihre Effizienz im Umgang mit Messdaten kontinuierlich zu verbessern. Dazu bieten wir Ihnen mit jBEAM, MaDaM, jBEAM-Cluster und iBEAM maßgeschneiderte Anwendungen zur Messdatenauswertung und -visualisierung. Diese unterscheiden sich dabei sehr von den meisten anderen Daten im Big Data-Bereich. Wir haben deshalb den Begriff BIG TEST DATA geprägt und berücksichtigen damit die Besonderheit von Messdaten.
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