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Authors: Matthias Volk ; Daniel Staegemann ; Sascha Bosse ; Robert Häusler and Klaus Turowski

Affiliation: Magdeburg Research and Competence Cluster Very Large Business Applications, Faculty of Computer Science, Otto-von-Guericke University Magdeburg, Magdeburg, Germany

Keyword(s): Big Data, Data Science, Engineering, Process.

Abstract: For many years now, researchers as well as practitioners are harnessing well-known data mining processes, such as the CRISP-DM or KDD, to realize their data analytics projects. In times of big data and data science, at which not only the volume, variety and velocity of the data increases, but also the complexity to process, store and manage them, conventional solutions are often not sufficient and even more sophisticated systems are needed. To overcome this situation, in this positioning paper the (big) data science engineering process is introduced to provide a guideline for the realization of data-intensive systems. For this purpose, using the design science research methodology, existing theory and current literature from relevant subdomains are contextualized, discussed and adapted.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Volk, M.; Staegemann, D.; Bosse, S.; Häusler, R. and Turowski, K. (2020). Approaching the (Big) Data Science Engineering Process. In Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - IoTBDS; ISBN 978-989-758-426-8; ISSN 2184-4976, SciTePress, pages 428-435. DOI: 10.5220/0009569804280435

@conference{iotbds20,
author={Matthias Volk. and Daniel Staegemann. and Sascha Bosse. and Robert Häusler. and Klaus Turowski.},
title={Approaching the (Big) Data Science Engineering Process},
booktitle={Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - IoTBDS},
year={2020},
pages={428-435},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009569804280435},
isbn={978-989-758-426-8},
issn={2184-4976},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - IoTBDS
TI - Approaching the (Big) Data Science Engineering Process
SN - 978-989-758-426-8
IS - 2184-4976
AU - Volk, M.
AU - Staegemann, D.
AU - Bosse, S.
AU - Häusler, R.
AU - Turowski, K.
PY - 2020
SP - 428
EP - 435
DO - 10.5220/0009569804280435
PB - SciTePress