Keywords: conceptual modelling

Keywords: DMN, conceptual modeling, machine learning, artificial intelligence, model-driven engineering
Astract: Since the last decade there has been a rapid rise in the use of BPMN (Business Process Model and Notation) standard in modeling of business processes. However, BPMN may be impractical due to its complexity and weak interoperability between business process tools. Recently, the Decision Model and Notation (DMN) standard has been introduced by OMG (Object Management Group), which is able to simplify the latter standard for decision modeling and/or multi-criteria decision-making. The purpose of DMN is to be readable and adjustable for people from business, as well as IT, respectively. The advances of technology and innovation have led to emerging big data analytics and new computational methods. Machine Learning tools are essential for the maximum utilization of the information in decisions makers. Data-driven technologies and BPMN both provide powerful tools, however according to the state-of-the-art there is no solution for coupling them in a synergistic manner. In addition, automation of modeling, using the DMN standard and the application of Machine Learning tools in this domain is still a challenge as modeling in the DMN standard requires manual steps, and ML tools are not natively supported by it. Therefore, in this thesis a Toolchain is proposed for tackling the above mentioned issues. The Thesis presents the design steps of the proposed solution. The input of the Toolchain can be either raw field data or alternatively a generated test case set from a DMN model. The proposed Toolchain implements the following three consecutive automated levels: Statistical Analysis with data preprocessing, a modeling step with three distinguished modeling strategies, and lastly an Evaluation stage. The statistical analysis covers correlation analysis, identification of the distribution of the variables, etc. The modeling stage includes fitting linear, standard Machine Learning CART and ensemble-type XGBoost models. These models are capable to handle the various levels of relationships between variables from linear to highly non-linear, which may compensate for the deficiencies of the original DMN model, since it is rather intuitive and may contain several overlapping or inefficient decision rules due to the manual creation of decision boundaries. The output of the Toolchain is a human readable result package, including the statistical analysis, the model performance evaluation and other partial results. The results obtained from experiments on a big data and a smaller insurance dataset confirms the applicability and validity of the proposed method. The results also indicate that the XGBoost model due to its outstanding performance is a suitable candidate for applying in a DMN standard instead of, e.g., a decision table. Furthermore, ML-based decision models would provide more flexibility and adaptivity that may result in easier automation of the decision process. Benchmarking in the context of execution and training times are also performed with special regard to the model complexity. The designed Toolchain aims to bridge the gap between ML and the DMN standard. Besides, the Thesis may provide valuable insights to the domain experts’ to better understand their models and empower decision makers with a different views on modeling.

Keywords: model engineering, language server protocol, visualization
Astract: With an increasing complexity level of modern software systems and their development comes a need for a more efficient use of modeling languages. In the recent years, modeling tools have started to shift from the traditional rich client approach to lighter and more decoupled systems, and with that, use more modern technology stacks, such as that of the web. One of such environments is the Eclipse Graphical Language Server Platform, which utilizes the concept of the language server protocol to divide a modeling environment into client and server. Nevertheless, working with spatially large models is still often inconvenient and cumbersome. Even most modern tools offer few means to effectively visualize and interact with large models.This work addresses these problems in two major steps. The first step was to find appropriate means that are able to increase the productivity while working with large models. In order to achieve that, we looked at features and existing research that deal with the visualization and interaction of large information. Furthermore, it presents a taxonomy which aids in the classification and evaluation of such features among three meta-characteristics. Based on these findings, two features were picked that were then conceptualized and integrated into a graphical language server platform in the second step. The first feature, semantic zooming, deals with the dynamic graphical adjustment of visible information based on the current zoom level. The second feature, visualizing off-screen elements, mainly provides a more efficient interaction with elements that are currently off screen. With the conclusion of the second step, this work provides a concept for the integration of both features into a GLSP-based environment. Additionally, it validates both concepts by providing a successful realization of their integration into the Eclipse-GLSP in the form of two prototypes.

Keywords:
Astract: This paper is concerned with the question of how novel digital technologies can be used to enable IT governance to better deal with the need for more agility, flexibility, adaptivity, and connectivity, as brought about by our modern day society. We propose to digitally transform IT governance, in particular making it smart(er) by following a data-driven approach. In line with this, we present a vision for digitally transformed IT governance in the form of the DT4GITM (Digital Twin for Governed IT Management) framework, which exploits the Digital Twin concept as it is already used in other fields to monitor, analyze, simulate, and predict the performance of real-world assets. The purpose of the DT4GITM framework is to serve as a reference architecture for a technological infrastructure based on the Digital Twin concept that connects three interrelated systems - the IT governance processes, the governed IT management processes, and the managed organizational IT assets.

Keywords: Software, Information Systems, Consistency, Multi-view modeling, Risk-aware business process management, Meta-modeling
Astract: Risk-aware Business Process Management (R-BPM) has been addressed in research since more than a decade. However, the integration of the two independent research streams is still ongoing with a lack of research focusing on the conceptual modeling perspective. Such an integration results in an increased meta-model complexity and a higher entry barrier for modelers in creating conceptual models and for addressees of the models in comprehending them. Multi-view modeling can reduce this complexity by providing multiple interdependent viewpoints that, all together, represent a complex system. Each viewpoint only covers those concepts that are necessary to separate the different concerns of stakeholders. However, adopt- ing multi-view modeling discloses a number of challenges particularly related to managing consistency which is threatened by semantic and syntactic overlaps between the viewpoints. Moreover, usability and efficiency of multi-view modeling have never been systematically evaluated. This paper reports on the conceptualization, implementation, and empirical evaluation of e-BPRIM, a multi-view modeling extension of the Business Process-Risk Management-Integrated Method (BPRIM). The findings of our research contribute to theory by showing, that multi-view modeling outperforms diagram-oriented modeling by means of usability and efficiency of modeling, and quality of models. Moreover, the developed modeling tool is openly available, allowing its adoption and use in R-BPM practice. Eventually, the detailed presentation of the conceptualization serves as a blueprint for other researchers aiming to harness multi-view modeling.