Keywords: conceptual modelling

Keywords: conceptual modelling

Keywords: Business Process Modeling (BPM)

Keywords: conceptual modelling

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: Theia, EMF Compare, Diagram diff merge, Cloud based IDE
Astract: The last decade has brought about countless advancements in the field of technology. Where once software development was a toilsome process done in plain text editors, lacking any sort of syntax highlighting, validation or other means of aiding the developer, now IDEs and specially designed code editors reign supreme in this field. Long gone are the days when building and running even the simplest pieces of software, required the developer to undertake a series of tasks. Now most of these tasks can be achieved through the push of a button in the IDE. Be it called "Run", "Execute" or "Start", the overall functionality of that button is clear: turn the code from text in an editor to a running program that achieves its goal. Aside from the usability aspect, the last few years have seen improvements in other areas, with formerly niche methodologies becoming more and more mainstream, such as Model-driven development, or MDD for short. Furthermore the emergence of cloud-based IDEs means that developers are no longer bound by the computational capabilities of the machines that are physically available to them. The client-server architecture of such IDEs allows devices with modest specifications to delegate the more intensive tasks to a server and merely concern themselves with sending the instructions and displaying the results. It is at the intersection between the fields of cloud-based IDEs and MDD where this thesis makes its contribution. MDD allows developers to build complex applications while maintaining a high level of abstraction, and to quickly make changes without concerning themselves with code-specific issues. Because complex applications often have correspondingly complex models behind them, it is often the case that more than one developer will be working on a model and/or diagram of a system. This raises the need for collaborative modeling support for diagram-based representations, in a similar way to the text-based support offered by tools such as Git or SVN, which allow different versions of a file to be compared and merged with each other. This thesis analyzes the available support for diagram diffing and merging both within the most commonly used cloud-based IDEs, as well full-fledged IDEs, and provides an implementation of its own for the Theia cloud-based IDE. Already existing, and well established components and frameworks have been reused wherever suitable, thus presenting users with somewhat known diff visualization means, such as side by side comparisons, as well as merging mechanisms. The computation of the comparison as well as the merging process are being handled by the EMF Compare framework, with the focus of this thesis lying on the visualization of the diffs and means of conflict resolution and diagram merging. The results have been evaluated in regard to the efficiency of the protocol facilitating the communication between the frontend and the backend components of the implementation, as well as in regard to the usability of the developed visualization and merging / conflict resolution means. This evaluation has been conducted by way of benchmarking in case of the communication protocol while the System Usability Scale has been used to measure the usability of the implementation. The results themselves show that the developed diffing and merging capabilities were easy to use and sufficient for daily tasks for a majority of respondents, scoring between good and excellent on the System Usability Scale.

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: Model Driven Engineering, Model Engineering, Pattern Recognition, Machine Learning, Clustering, Dual Deep Instantiation, Sensor Systems, Model Intelligence, Model Generation, Monitoring Systems
Astract: This thesis examines the possibility of model generation from data sets. Automatic model generation from data sets is a subtopic of Model Intelligence. Model Intelligence aims to combine the benefits of Model Driven Engineering (MDE) with the benefits of machinelearning (ML) techniques. During a workshop about Model Intelligence a call for paper,namely "Model inferencers and automatic model generators from datas ets" [ MDE ]. In this work we propose a method to generate models with the help of machine learning from data sets and make a case study on a real-life data set coming from a sensor system of the (Plus-)Plus-Energy building of the Vienna University of Technology. Different definitions of"model" are considered to answer the question whether or not it is possible to generate models automatically from sensor data with the help of machine learning and data mining techniques.A frequent pattern recognition based method is introduced, which aims to generate a frequent pattern based model for sensors. To set boundaries for model generation, we discuss the basic concepts and introduce key techniques of metamodeling. We choose a suitable technique to define our metamodel, which then will serve as the basis of model generation. During defining the model and exploring different modeling techniques we mainly discuss methods and related concepts conforming to the Meta-Object Facility (MOF).After defining the model, we apply segmentation on the sensor-derived data set, and label different segments according to the Symbolic Aggregate approXimation (SAX) We apply frequent pattern mining on the segmented and labeled data sets. Based on the results of the patternmining, we carry out clustering to help us decide, whether the frequent patterns found in the sensor data are suitable to differentiate between different types of sensors, thus can serve as attributes in model definitions for the different types of sensors.Our results show that depending on what definition of "model" is used, it is possible to generate models either on the M1 or the M0 level of the M4 meta-modeling framework. Furthermore, aproposed model with Double Deep Instantiation (DDI) is feasible to model type definitionsfor the sensors. In conclusion, we show that generating type definitions for the sensors based on their frequent patterns is successful in most cases. We have found some instances with interesting caveats that call for further investigation.ix