Big Data Computational Environment for Tomography Measurement Data

Romanowski Andrzeja, Skuza MichaŁa, PaweŁ Woźniakb, Grudzieł Krzysztofa,

Chaniecki Zbigniewa

aInstitute of Applied Computer Science, Lodz University of Technology, Poland

b t2i Interaction Laboratory, Chalmers University of Technology, Sweden

Abstract

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.

Keywords: image processing; Big Data Analysis; ECT; Measurement Data Processing; Computational Environment; Machine Learning Alogrithms