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.