Strategic business IT alignment has been conceptualized and researched through two distinctly different approaches, both with weaknesses when considered from the practitioner perspective. The first from strategic management research assesses "fit" quantitatively as a holistic concept, but cannot open up the underlying enterprise design logic. The second from architecture and engineering method research is focused on the enterprise design in full, and as a consequence overwhelms in detail. Both lack an organizing foundation for developing cumulative knowledge. Our objective is to derive a way forward, by zooming in on the alignment decisions that practitioners perform. Adopting a design science research method, we propose a new domain-based conceptualization that matches practitioner competency areas, with alignment reasoning across. Our operationalization results in three artifacts: domains cover coherent areas of subject matter that reduce contingencies, alignment artifacts envelope underlying designs and extract essential alignment attributes that suppress irrelevant detail, and a knowledge model provides the organizing template for accumulation of actionable knowledge connected to domains and artifacts. We evaluate our approach using criteria for artifact soundness, elaborating a case from practice, populating the knowledge model with existing artifact centric research, and expert interviews. We conclude that our proposed approach takes the middle ground and can integrate with both existing approaches, and provides an excellent case for further research into the nature and structure of theorizing in the broader IS.
Analyzing the execution behavior of Ethereum Decentralized Applications (DApps) with process mining presents significant challenges due to the multi-object nature of DApp data. Traditional event logs, like XES, struggle to capture the respective structures and interactions effectively. This paper proposes a method for extracting DApp execution data from Ethereum and representing it in the Object-Centric Event Log (OCEL) 2.0 format. We address central challenges in this process, including dynamic contract deployments, preserving the order of operations within transaction traces, and accurately representing object types and their evolving roles. Our findings demonstrate that, while OCEL 2.0 offers some advantages for capturing the rich interactions within DApps, certain limitations regarding hierarchical object types and event granularity require workarounds. We evaluate the practicality of our approach with a case study of the prediction market platform Augur, highlighting how object-centric process mining can provide insights into DApp behavior. This work contributes to a better understanding of object-centric process mining in the context of blockchain data.
The Work System Theory (WST) is a foundation theory that enables analysis of systems in organizations. It encompasses a set of concepts that help describing, analyzing, designing and evaluating purposeful systems that perform work. A WST-based method guides a work system's analysis through the identification of problems/opportunities, summarizing the As -Is and To -Be versions of a system. Motivated by a Design Science project running in an academic institution, we explore in this paper the application of graph -based semantic technologies to specify and analyze work systems and to bridge their design -time view with run-time data found in legacy systems. Our contribution is twofold (1) we propose an ontological schema that informs RDF-based knowledge graph building with a Work System perspective; (2) we demonstrate some benefits of having work systems represented as Knowledge Graphs that are linked to operational data and further subjected to semantic queries and deductive reasoning. The Design Science artifact is iteratively developed in the host institution of the first authors and builds on previous development of a knowledge graph that has been lifted from legacy databases. The graph -based approach is viable to bridge an inherent conceptual gap between the work systems conceptualization and operational data schemas, thus adding value both to decision -making and to run-time systems that will be later built to benefit from the semantic distinctions present in the resulting graph.
Supply Chain (SC) integrated modeling is required for visibility and proactive monitoring of members and processes across the SC network. Recent works have established SC models incorporating core relations and structures. However, such models are still rather isolated, thus preventing a holistic view of the SC. We identify a lack of End -to -End (E2E) SC data that enables integrated analysis of the SC. Existing logs or data from one company are not enough to validate the E2E SC models. We present SENS, a standardized integrated semantic model that provides an overall view of SCOR E2E SC structure and flows. This vocabulary is used to generate synthetic SC data compensating for the scarcity of the overall benchmarking data via SENS-GEN. The evaluation shows that the significantly improved simulation and analysis capabilities, enabled by SENS, facilitate grasping, controlling and ultimately enhancing SC behavior and increasing resilience in disruptive scenarios.
Enterprise Knowledge Graphs (EKGs) are increasingly created and used by organizations for structuring knowledge of a particular application domain and consequent exploitation through analysis, reasoning and integration of information extracted from different data sources. Yet, one of the main challenges is designing and maintaining EKG's schema, which requires high expertise in ontology engineering and the addressed application domain. Various approaches and tools offer visual aids, but they target ontology engineers and neglect the domain experts. Domain-specific modelling languages (DSML), in contrast, offer concepts that domain experts easily understand because of their tailored graphical notations. DSMLs can be created with a meta-modelling approach, which does not require ontology expertise but can be adopted for the equivalent creation of EKG schema. This paper presents an approach that extends the traditional and sequential meta-modelling approach with an agile one, allowing domain-specific adaptations of modelling languages and testing them on the fly. In this way, the domain experts are facilitated to be in the engineering loop. Moreover, the approach foresees automatic mechanisms to ensure that an EKG schema is designed while performing the visual domain-specific adaptations. The approach has been developed by following the Design Science Research methodology, which led to the creation of a prototypical tool called AOAME. The latter has been used to implement real-world scenarios to evaluate the proposed approach's utility. The correct design of the approach has been evaluated by tracing the prototype functionalities back to the requirements.
To obtain more financial freedom, universities and especially their chairs and institutes have to establish a well functioning and reliable financial management and accounting system. Currently, chairs have different technical solutions for these systems, each of which must react individually to external changes and require a high effort to adapt their reports. Thus, they rely on either commercial accounting software, which is not tailored to their specific needs, or standard spreadsheet software making use of complex sheets and cross-references which are error -prone and hard to adapt. We have used domain models and code synthesis methods to create an enterprise information system. This paper shows how models reflect user requirements, evolve with changing requirements and how they impact an agile, model -driven engineering process. The resulting system simplifies the planning of financial management and accounting by university chairs.
Established companies intending to leverage digital technologies are required to innovate their 'legacy' business models through organizational transformations. Existing modeling support often leaves a 'white space' between informal canvas -style models used in the early phases and (semi -)formal aspect models used in the later phases of transformation endeavors. Adopting a Design Science Research approach, this paper presents a semi -formal model that is intended to fill this gap, i. e., to provide easy -to -use support for the heterogeneous stakeholders that participate in early phases of digital transformation endeavors. Being based on the traditional Business Engineering set of models and methods, this comprehensive and collaborative approach was validated together with the digital transformation program manager of a large, international corporation. For supporting analysis, reflection and design tasks that involve a broad range from canvas -style models to enterprise architecture models, four requirements were identified to be central: (remote) collaboration support, a holistic and integrative perspective, an enterprise -level view, and a focus on change. The actual model is comprised of over 20 partial models including popular canvases depicting the transformation program's content on the strategy -to -IT layers, the enterprise and local level, and in the as -is and to -be state. Demonstration and evaluation were done with practitioners and students of an Executive Master program focusing on digital transformation. Both confirmed the utility of the underlying method and recognized its distinctive features, while capability and IT landscape models were found especially relevant. The method is expected to be applicable for digital transformations beyond the case and also to be projectable to smaller -scaled digital business innovations.
Design Science Research (DSR) is a well-established paradigm in the Information Systems field generating knowledge on the design of innovative solutions to real-world problems. The maturity of DSR has increased due to many methodological contributions, including conceptualization of the design process, templates on how to plan and document, as well as guidelines on how to conduct DSR projects. At the same time, given the dynamic nature of design in the digital era, DSR methods are also constantly further developed by the community. Both access to existing DSR methods and its further development are hindered today by the way we represent DSR methods. Most of the DSR methods are scattered in different papers or books. In order to foster accessibility and further development, we propose a harmonized representation of DSR process knowledge (as a core component of DSR methods) in an open repository. Applying DSR ourselves, we 1) identify meta-requirements for a DSR process modeling system 2) derive initial design principles 3) propose a meta-model 4) provide an instantiation of the meta-model in the form of an open repository, and 5) evaluate our design based on interviews with DSR researchers using the repository. We report from two DSR cycles, then discuss our findings and outline avenues for future research.
With the introduction of the Language Server Protocol (LSP), a fundamental shift has been observed in the development of language editing support for Integrated Development Environments (IDEs), such as VS Code, the traditional Eclipse IDE, or Eclipse Theia. LSP establishes a uniform protocol that standardizes the communication between a language client (e. g., an IDE like Eclipse) and a language server (e. g., for a programming language like Java). The language client only needs to be able to interpret and understand the protocol instead of the specific programming language. Likewise, the language server can focus on language support and does not need to consider the specifics of a respective IDE. This reduces the complexity of realizing language support on different editors and IDEs and enables smooth transitions from one IDE to another. LSP is an open and community-driven protocol that has been developed within the realm of the VS Code community, initiated and driven by Microsoft. The generic concept and architectural pattern of LSP enables widespread applications that go far beyond the realization of editing support for programming languages. This paper provides an introduction to LSP, describes its evolution and core characteristics, and delineates its potential for revolutionizing not only the IDE market but also other software systems, such as modeling tools.
. The reform of the European academic landscape with the introduction of bachelor’s and master’s degree programs has brought about several profound changes for teaching and assessment in higher education. With regard to the examination system, the shift towards output-oriented teaching is still one of the most significant challenges. Assessments have to be integrated into the teaching and learning arrangements and consistently aligned towards the intended learning outcomes. In particular, assessments should provide valid evidence that learners have acquired competences that are relevant for a specific domain. However, it seems that this didactic goal has not yet been fully achieved in modeling education in computer science. The aim of this study is to investigate whether typical task material used in exercises and exams in modeling education at selected German universities covers relevant competences required for graphical modeling. For this purpose, typical tasks in the field of modeling are first identified by means of a content-analytical procedure. Subsequently, it is determined which competence facets relevant for graphical modeling are addressed by the task types. By contrasting a competence model for modeling with the competences addressed by the tasks, a gap was identified between the required competences and the task material analyzed. In particular, the gap analysis shows the neglect of transversal competence facets as well as those related to the analysis and evaluation of models. The result of this paper is a classification of task types for modeling education and a specification of the competence facets addressed by these tasks. Recommendations for developing and assessing student’s competences comprehensively are given.
. Innovation in enterprises require an understanding of the inherent complexity and dependencies among an enterprise’s processes, goals, resources, customers and several other aspects. The choice of a suitable Enterprise Modelling method and language is an essential part of creating this understanding. This paper presents an overview of students’ perspectives on the Enterprise Modelling methods and languages 4EM and ArchiMate, and the patterns and criteria for selecting a specific method and modelling language for their assignments. The analysis is based on a post hoc analysis of students’ assignments from a course in Enterprise Architecture and Innovation, over a period of four years. The main contributions of this work are a set of selection criteria and recommendations for educators and students in selecting Enterprise Modelling methods and languages.
. This paper presents the teaching experience of a course named Information Systems Engineering in a Master’s degree program of the Universitat Politècnica de València. The target of this course is to teach Model-Driven Development (MDD). On the last years we have observed that students attended the course with poor motivation since they do not see MDD as being a useful development paradigm. The students have an extensive background in a traditional method (they are good programmers) where all the code is manually programmed, but they lack sound experience in conceptual modeling. In order to improve their motivation and to highlight the pros and cons of MDD, we propose a practical comparison of a traditional method and MDD. The teaching methodology consists of a problem-based learning task where students must develop two problems from scratch, one with a traditional method and the other with MDD. Our experience has been evaluated in terms of attitude towards MDD, knowledge of MDD, quality of the developed system, and satisfaction of the developer. The results show that the students obtained significantly better results for MDD in terms of attitude, knowledge, and quality
Since OpenAI publicly released ChatGPT in November 20221 , many ideas have emerged as to which applications this type of technology could support. At its core, ChatGPT is a conversational artificial intelligence, meaning that it can engage in a dialogue to respond to user input given in natural language (Campbell 2020). Although such types of systems have been well-known since Weizenbaum’s Eliza program (Weizenbaum 1966) and are today widely deployed in practice under the popular term chatbots, ChatGPT has a particular set of properties that contributed to its wide reception and the recent hype surrounding it. In contrast to previous chatbots, ChatGPT does not retrieve responses from a knowledge base, which has been pre-defined by some human user. Rather, it is based on a pre-trained generative language model, which creates responses based on patterns that the user supplies as input. Thereby, a language model basically assigns probabilities to every word in a vocabulary that can follow a given input sequence. Such word embeddings are trained using artificial neural networks to learn a probability distribution from given texts in an unsupervised fashion, i. e. such that no additional human input or labeling is required. The generation of the output sequence thereby considers the tokens of the input sequence and their position as well as the previously generated output,
In Computer Science, Software Engineering, Business Informatics or Information Systems, conceptual modeling is an important tool and as such also contained in the respective curricular recommendations. Especially in large university courses, an automated assessment of models can improve the quality of teaching and learning. While there are many different approaches to automatically assess conceptual models, these approaches, however, often only tackle a single aspect or a single type of conceptual model. In this paper, we aim to take a comprehensive perspective on the topic and shed light on the current state of the art and technique. Furthermore, as assessment approaches have to be developed in accordance with appropriate teaching or learning activities and desired learning outcomes, we inquire in which settings automated assessment approaches are included and to which extent didactic aspects are taken into account. To this end, we have conducted a systematic literature review in which we identified 110 relevant publications on the topic which we have analyzed in a structured way. The results provide answers to five relevant research questions and pinpoint open issues which should be inquired in further research.
In December 1921, Frank B. and Lillian Moller Gilbreth held a presentation entitled "Process Charts" at the Annual Meeting of The American Society of Mechanical Engineers. They presented a diagrammatic notation for depicting work processes. This was the reason for initiating a call for papers for a special edition of the EMISA Journal. The aim of this issue is to reflect on the history of graphical business process modelling as well as on current and future challenges. In this editorial, we will shortly introduce the ideas behind the Process Charts method. We realize that some ideas discussed 100 years ago still remain highly relevant while modern work environment raises issues that would be unthinkable a century ago.
