Big data computational environment for tomography measurement data
Paper i proceeding, 2014

The authors propose a concept of computational environment for manipulation of big data sets originating from tomography-based measurement experiments. This work shows an example of utilizing the proposed tool in order to detect material plugs for pneumatic conveying measurement data. System is based on Hadoop distributed environment with Machout machine learning library. Paper presents results for a combination of horizontal and vertical flow experimental data coming from different experiments accumulated and processed in order to automatically detect plugs. Authors implemented supervised learning algorithms and naive Bayes classifier. Results show the possible way of using Big Data capabilities for both automatic data processing as well as for preparation of results for further analysis and interpretation by domain experts.

Machine Learning Alogrithms

Big Data Analysis

Computational Environment


Image processing

Measurement Data Processing


R. Andrzej

Politechnika Lodzka

S. Michał

Politechnika Lodzka

Pawel Wozniak

Chalmers, Tillämpad informationsteknologi, Interaktionsdesign (Chalmers)

G. Krzysztof

Politechnika Lodzka

C. Zbigniew

Politechnika Lodzka

7th World Congress in Industrial Process Tomography, WCIPT7; Krakow; Poland; 2 September 2013 through 5 September 2013



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