Decisions are of significant value to organisations. Business decisions are often written down in textual documents, and modelling them is a tedious and time-consuming task. Although decision modelling has seen a surge of interest since the introduction of the Decision Model and Notation (DMN) standard, limited research has been conducted regarding automatically extracting decision models from the text. In this paper, we propose a text mining technique to automatically extract the decisions and their dependencies from natural language text to build the decision requirements diagram. A case-based evaluation is shown for the proposed mining approach with promising results. This approach can serve as a groundwork for further research in the field of decision automation.

Digital transformation is the rapidly expanding research field dealing with the increased impact of digital technologies on both business and society. Due to the large number of papers and the semantic ambiguity surrounding the terminology, covering such a broad topic is difficult. To help researchers gain a better understanding of the knowledge structure of the research field, we conduct a scoping review using scientometrics. We searched for publications dealing with digital transformation on both Scopus and Web of Science. We downloaded their bibliometric data and thoroughly merged and cleaned it using lemmatization and stemmatization. This dataset was analyzed using VOSviewer to create co-author networks and co-word occurrence graphs of the titles, abstracts, and keywords. We also visualized the growth of the research field and retrieved the top conferences and journals based on the number of papers and the number of citations. K-means clustering was performed on the abstracts and keywords to find similar research focuses. These findings highlight the broad scope of the research field, the ambiguity of the terminology, the lack of collaboration, and the absence of research into the impact of digital transformation on society. Moving forward, more research needs to be done to establish the boundaries of digital transformation and to investigate the importance of society in this phenomenon.

Decisions are of significant value to organisations. Business decisions are often represented in various knowledge sources, and manually modelling them is costly, tedious and time-consuming. As decision modelling has seen a surge of interest since the introduction of the Decision Model and Notation (DMN) standard, research interest has also increased regarding automatically extracting decision models. In this paper, we discuss an overview and classification of such techniques, including generating decision models from various knowledge sources such as natural language text, legacy code, other models or event logs.

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Decision Model and Notation (DMN) models are user-friendly representations of decision logic. While the knowledge in the model could be used for multiple purposes, current DMN tools typically only support a single form of inference. We present DMN-IDPy, a novel Python API that links DMN as a notation to the IDP system, a powerful reasoning tool, allowing the knowledge in DMN models to be used to its fullest potential. The flexibility of this approach allows us to build intelligent tools based on DMN unlike any other execution engine.