Detailed Information on Publication Record

MACÁK, Martin, Hind BANGUI, Barbora BÜHNOVÁ, András J. MOLNÁR and Csaba István SIDLÓ. Big Data Processing Tools Navigation Diagram. Online. In Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS. Setubal, Portugal: SciTePress, 2020, p. 304-312. ISBN 978-989-758-426-8. Available from: https://dx.doi.org/10.5220/0009406403040312.

Other formats: BibTeX LaTeX RIS

Basic information
Original name	Big Data Processing Tools Navigation Diagram
Authors	MACÁK, Martin (703 Slovakia, belonging to the institution), Hind BANGUI (504 Morocco, belonging to the institution), Barbora BÜHNOVÁ (203 Czech Republic, belonging to the institution), András J. MOLNÁR (348 Hungary) and Csaba István SIDLÓ (348 Hungary).
Edition	Setubal, Portugal, Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, p. 304-312, 9 pp. 2020.
Publisher	SciTePress

Other information
Original language	English
Type of outcome	Proceedings paper
Field of Study	10201 Computer sciences, information science, bioinformatics
Country of publisher	Portugal
Confidentiality degree	is not subject to a state or trade secret
Publication form	electronic version available online
WWW	URL
RIV identification code	RIV/00216224:14610/20:00115478
Organization unit	Institute of Computer Science
ISBN	978-989-758-426-8
Doi	http://dx.doi.org/10.5220/0009406403040312
UT WoS	000615960700032
Keywords in English	Big Data; Processing Tools; Tool Selection; Navigation Diagram
Tags	firank_B, rivok
Tags	International impact, Reviewed
Changed by	Changed by: Mgr. Alena Mokrá, učo 362754. Changed: 27/4/2021 11:50.

Abstract
Big Data processing has become crucial in many domains because the amount of the produced data has enormously increased almost everywhere. The effective selection of the right Big Data processing tool is hard due to the high number and large variety of the available state-of-the-art tools. Many research results agree that there is no one best Big Data solution for all needs and requirements. It is therefore essential to be able to navigate more efficiently in the world of Big Data processing tools. In this paper, we present a map of current Big Data processing tools, recommended according to their capabilities and advantageous properties identified in previously published academic benchmarks. This map—as a navigation diagram—is aimed at helping researchers and practitioners to filter a large amount of available Big Data processing tools according to the requirements and properties of their tasks. Additionally, we provide recommendations for future experiments comparing Big Data processing tools, to improve the navigation diagram.

Abstract

Big Data processing has become crucial in many domains because the amount of the produced data has enormously increased almost everywhere. The effective selection of the right Big Data processing tool is hard due to the high number and large variety of the available state-of-the-art tools. Many research results agree that there is no one best Big Data solution for all needs and requirements. It is therefore essential to be able to navigate more efficiently in the world of Big Data processing tools. In this paper, we present a map of current Big Data processing tools, recommended according to their capabilities and advantageous properties identified in previously published academic benchmarks. This map—as a navigation diagram—is aimed at helping researchers and practitioners to filter a large amount of available Big Data processing tools according to the requirements and properties of their tasks. Additionally, we provide recommendations for future experiments comparing Big Data processing tools, to improve the navigation diagram.

Links
EF16_013/0001802, research and development project	Name: CERIT Scientific Cloud