Towards big data solutions for industrial tomography data processing

• Autorzy: Kowalska Aleksandra , Łuczak Piotr , Sielski Dawid , Kowalski Tomasz , Romanowski Andrzej , Sankowski Dominik

Identyfikatory

DOI

10.15439/2019F310

• Konferencja: Federated Conference on Computer Science and Information Systems (14 ; 01-04.09.2019 ; Leipzig, Germany)
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

big data; data acquisition; biomedical measurements; tomography

PL

big data; pozyskiwanie danych; pomiary biomedyczne; tomografia

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2019
• Tom: Vol. 18
• Strony: 427--431
• Opis fizyczny: Bibliogr. 26 poz., il.

Twórcy

autor

Kowalska Aleksandra

• Institute of Applied Computer Science, Lodz University of Technology, Łódź, Poland

autor

Łuczak Piotr

• Institute of Applied Computer Science, Lodz University of Technology, Łódź, Poland

autor

Sielski Dawid

• Institute of Applied Computer Science, Lodz University of Technology, Łódź, Poland

autor

Kowalski Tomasz

• Institute of Applied Computer Science, Lodz University of Technology, Łódź, Poland

autor

Romanowski Andrzej

• Institute of Applied Computer Science, Lodz University of Technology, Łódź, Poland

autor

Sankowski Dominik

• Institute of Applied Computer Science, Lodz University of Technology, Łódź, Poland

Bibliografia

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Uwagi

1. This work is partially financed by the Smart Growth Operational Programme 2014-2020 project no POIR.04.01.02-00-0089/17-00. The project is conducted in the Institute of Applied Computer Science at the Lodz University of Technology.

2. Track 2: Computer Science & Systems

3. Technical Session: 10th Workshop on Scalable Computing

4. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).