Towards Big Data Solutions for Industrial Tomography Data Processing
Aleksandra Kowalska, Piotr Łuczak, Dawid Sielski, Tomasz Kowalski, Andrzej Romanowski, Dominik Sankowski
Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 427–431 (2019)
Abstract. This paper presents an overview of what Big Data can bring to the modern industry. Through following the history of contemporary Big Data frameworks the authors observe that the tools available have reached sufficient maturity so as to be usable in an industrial setting. The authors propose the concept of a system for collecting, organising, processing and analysing experimental data obtained from measurements using process tomography. Process tomography is used for noninvasive flow monitoring and data acquisition. The measurement data are collected, stored and processed to identify process regimes and process threats. Further general examples of solutions that aim to take advantage of the existence of such tools are presented as proof of viability of such approach. As the first step in the process of creating the proposed system, a scalable, distributed, containerisation-based cluster has been constructed, with consumer-grade hardware.
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