Lowering Big Data Project Barriers: Identifying System Architecture Templates for Standard Use Cases in Big Data

Matthias Volk, Daniel Staegemann, Akanksha Saxena, Johannes Hintsch, Naoum Jamous, Klaus Turowski

2022

Abstract

For almost a decade now, big data has become the foundation of today’s data-intensive systems used for various disciplines, such as data science or artificial intelligence. Although a certain level of maturity has been reached since then, not only in the domain itself but also in the engineering of interconnected systems, many problems still exist today. The number of available technologies and architectural concepts, whose application is often very use case-specific, makes the successful implementation of big data projects still a non-trivial undertaking. To overcome this problem and deliver support with the realization of a related project, existing standard use cases in this domain are analyzed, and architectural concepts are derived through the design science research methodology. By observing essential criteria, like use case descriptions as well as relevant requirements, decision-makers can harness architectural concepts and technology recommendations for their setup.

Paper Citation

in Harvard Style

Volk M., Staegemann D., Saxena A., Hintsch J., Jamous N. and Turowski K. (2022). Lowering Big Data Project Barriers: Identifying System Architecture Templates for Standard Use Cases in Big Data. In Proceedings of the 19th International Conference on Smart Business Technologies - Volume 1: ICSBT, ISBN 978-989-758-587-6, pages 33-44. DOI: 10.5220/0011307700003280

in Bibtex Style

@conference{icsbt22,
author={Matthias Volk and Daniel Staegemann and Akanksha Saxena and Johannes Hintsch and Naoum Jamous and Klaus Turowski},
title={Lowering Big Data Project Barriers: Identifying System Architecture Templates for Standard Use Cases in Big Data},
booktitle={ Proceedings of the 19th International Conference on Smart Business Technologies - Volume 1: ICSBT,},
year={2022},
pages={33-44},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011307700003280},
isbn={978-989-758-587-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Smart Business Technologies - Volume 1: ICSBT,
TI - Lowering Big Data Project Barriers: Identifying System Architecture Templates for Standard Use Cases in Big Data
SN - 978-989-758-587-6
AU - Volk M.
AU - Staegemann D.
AU - Saxena A.
AU - Hintsch J.
AU - Jamous N.
AU - Turowski K.
PY - 2022
SP - 33
EP - 44
DO - 10.5220/0011307700003280