Peters, G. & Weber, R. (2018). dynXcube – categorizing dynamic data analysis. Information Sciences,463-464W. Pedrycz. 21-32. United States of America: Elsevier. Retrieved from https://doi.org/10.1016/j.ins.2018.06.026
Data analysis has gained strategic importance for virtually any organization. It covers areas like business analytics, big data, business intelligence, and data mining, among others. The past decades have also witnessed increasing efforts to capture, analyze, and interpret dynamic data instead of just static snapshot data. This is due to the fact that many real-life applications are characterized by changing data structures. Hence, analytic systems need to be able to adapt to changes. In recent years, many models for dynamic data analysis have been proposed and successfully applied in a diverse range of real-life projects. Since the number of respective algorithms has continuously risen, it has become increasingly demanding to keep track in this field. This is not only related to the algorithms that have been proposed so far and their relationships to each other. It also applies to the disclosure of gaps in research that need to be filled by appropriate new algorithms and, therefore, uncover new research opportunities. To contribute to the review of this field, we propose a holistic framework to categorize dynamic data analysis, the dynXcube-Framework. We show that dynXcube is very useful to present the state-of-the-art of dynamic data analysis in a consolidated way. Furthermore, it has the potential to disclose gaps in current research, thus providing a road map for future activities in the field of dynamic data analysis. Therefore, dynXcube is a significant step towards an improved accessibility of dynamic data analysis methods for academics and professionals alike and will help to stimulate future research in this important field.
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