Research Report: Cornerstone Big Data Analysis through Apache Spark

How can different data buckets be integrated and flexibly analyzed using Big Data techniques?

The systematic analysis of ever-increasing data collection presents companies with ever-greater challenges. Many companies simply lack the know-how to handle big data projects. Following to the motto “Let’s do a Data Lake first”, they bring together all available data in one system. Because often they are subject to the misconception that you should put as much data as possible in the system in order to gain the maximum insight and the most flexible evaluations. Unfortunately this does not work, because we can expect performance problems here.

This research report summarizes a part of the work performed in the PRO-OPT SMART-DATA research project. In the project a wide variety of production data modeling approaches of an automotive supplier were tested out. One of the objectives was to be able to apply and compare statistically reliable analyses and classification procedures as well as new procedures from the upcoming AI instruments. The work is summarizes in this report.

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Variant Minimizing

Whitepaper: Variant minimizing Designs

How can the amount of experiment runs be reduced via minimizing the variants?

The cost for the runs of a design is dominated by a subset of factors. In this whitepaper an example with  7 factors is considered with 4 factors which account for most of the costs / efforts. Typical situations are factors which need to be made by prototype manufacturing whereas the other factors are different settings the hardware variants are used in.

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Whitepaper: Configurations for D-optimal Designs

How to determine the optimal settings for D-Optimal Designs in Cornerstone?

Designed experiments in Cornerstone are often based on D-optimal designs which are flexible enough to handle special situations. The criteria for the design, namely the determinant of the information matrix after rescaling the factors to standard range fits well to the embedding theory and its reciprocal is often called generalized variance. In addition, a maximized determinant minimizes the coefficient confidence intervals. The construction of D-optimal designs relies on a iterative algorithm which uses a list of settings and conditions from the planned experiment under consideration.

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Use Case example: Optimizing Factors in Experimental Design

How to use Cornerstone’s DoE module and regression to perform an optimization on a factor

In many designed experiments, factors with a known high influence on the response(s) are varied. As an example, the factor throughput of a combine harvester is used. The response considered are the loss of grain material or its quality. Other factors are adjustments to the machine and / or technical variants which usually have only a smaller effect on the response compared to the dominating factor throughput. In Cornerstone, regression models can be used to define targets for response variables that are to be achieved by varying the factors. In the example considered here, however, the factor throughput is to be maximized for a defined level of the response variable. Levers for this goal are the remaining factors.

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Product retrospective: Cornerstone Statistics for Engineers

A brief history and a summary of the most beneficial functionalities

Cornerstone is a proprietary software of the independent software vendor camLine GmbH. The software has a long tradition reaching back to the year 1991. It primarily is designed for engineers in research and development or in manufacturing who need a tool for applied statistics. Cornerstone has a special focus on the fields of Analyses by Regression, Exploratory Data Analysis (EDA), quality control (control charts, process capability), and Design of Experiments (DoE).

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