Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/312458 
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Erscheinungsjahr: 
2022
Quellenangabe: 
[Journal:] Information Systems and e-Business Management [ISSN:] 1617-9854 [Volume:] 21 [Issue:] 1 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2022 [Pages:] 193-227
Verlag: 
Springer, Berlin, Heidelberg
Zusammenfassung: 
Taxonomies can serve as a valuable tool to capture dimensions and characteristics of data analytics solutions in a structured manner and thus create transparency about different design options of the technical solution space. However, previous taxonomic approaches often remain at a purely descriptive level without leveraging morphological structures to investigate the mechanisms between different combinatorial options given in data analytics pipelines. To this end, we propose a taxonomic evaluation approach to evaluate and construct the technical core of analytical information systems more systematically. Specifically, we present a rough guidance model consisting of four steps, which we subsequently instantiate with two application scenarios from the fields of industrial maintenance and predictive business process monitoring. In this way, we demonstrate how taxonomic frameworks can guide the creation of structured evaluation studies to consider the construction and assessment of data analytics pipelines in a multi-perspective and holistic manner. Our approach is sufficiently generic to be applied to various domains, scenarios, and decision support tasks.
Schlagwörter: 
Data analytics
Taxonomy development
Evaluation framework
Ablation and substitution studies
Predictive maintenance
Predictive business process monitoring
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