On the Complementarity between CMMN and iStar in Complex Domain Modeling

Case Management Modeling and Notation (CMMN) and iStar are two distinct, multi-purposed modeling techniques that may be used to represent organizational challenges at separate levels of abstraction. CMMN, a flexible process-oriented technique, aims to extract knowledge that enhances the representational capacity of activity flows for a specific case. Conversely, the iStar framework adopts a goal-oriented modeling approach, effectively capturing the interplay of social actors and their influence on the attainment of organizational objectives. While prior studies have explored methods for integrating these techniques to attain a more comprehensive understanding of the organizational landscape, these remain primarily associated with a distinct level of the organizational matrix (i.e., CMMN for operations and tactics, and iStar for more strategic aspects). As such, any effort to evaluate their semantic proximity appears fragmented, as it deals only with the partial association of specific notations and elements. This article describes the conduct of a dual-purposed literature review to identify specific criteria that might accommodate a more holistic assessment of the two modeling techniques; these criteria are then employed as a guiding framework to construct a set of propositions that articulate the areas of complementarity and/or divergence between these two techniques, as identified in previous research. These propositions are subsequently subjected to validation by domain experts, leveraging a real-world case study in the educational domain. The results show that there can be areas of semantic convergence between the two techniques, suggesting their parallel use to effectively model complex domain problems. Overall, the present study aims to crystallize an approach for conducting complex modeling comparisons that transcends technical considerations

Information Security and Privacy Management in Intelligent Transportation Systems

With the global digitalization of services, passenger Intelligent Transportation Systems (ITSs) have emerged as transformative components, yet the practical implementation of state-of-the-art measures to ensure information security and privacy presents substantial challenges. In this article, we propose a framework for information security and privacy management. The framework is validated through two empirical studies. First, the framework is used to extract data during the literature review defining state-of-the-art aspects and measures. Second, a survey-based analysis of running passenger ITSs in selected regions of the European Union provides insights into real-life ITS implementations, enabling a thorough comparison with the proposed state-of-the-art measures. The study also showed that the proposed framework depicts some dependencies between measures, and, thus, using its matrix structure for the state of information security and privacy management in the organization helps to cross-check the usage of policies or methodologies by the organization departments. Our findings resulted in recommendations for organizations developing ITSs to enhance their information security and privacy management systems and bridge the gap between research proposals and practical implementation

Variability Modeling in Enterprise Architecture Management: Case Study and Survey on Existing Approaches

Managing and dealing with variability in business processes and the IT landscape is a common challenge in the everyday practice of most enterprises and organizations. Recent studies have observed that digital transformation, Internet-of-Things solutions and the introduction of artificial intelligence cause changes and challenges in enterprises that simultaneously require variability on several levels, for instance, business processes, data architecture, and services. Enterprise architecture models are considered a suitable way to visualize and manage dependencies between different levels of an enterprise. However, the management of variability in enterprise architectures has not received much attention in scientific research. This article aims to contribute to a better understanding of future investigation needs. Using a systematic literature analysis, the article structures the existing research work in the field and examines real-world challenges of variability based on a case study. We argue that there is a need for new constructs in enterprise architecture models that allow for expressing dependencies between variations on different enterprise architecture layers

CSIMQ Anniversary Editor-in-Chief Thoughts and Editorial Introduction to Issue 38: Model-based and Decision Support Methods for Next-generation Information Systems

The 38th issue of CSIMQ comprises four articles selected by guest issue editors on topics related to novel decision support methods or model-based frameworks for evolving or evaluating information systems. Design-oriented research is the dominant approach in these works, balancing technical design decisions insights with empirical evaluation cases. Three of the selected articles contribute with decision-support methods or frameworks – for ESG (Environment-Social-Governance) accounting, for democratized decision services, and for information security management. The fourth article revisits UML-based model-driven software development from a new perspective

Navigating the Complex ESG Accounting Landscape: Engineering a Method Selection Framework

Environmental, social, and governance accounting (ESGA) aids organizations in achieving their sustainability goals through continuous improvement. Suitable method selection is crucial to prevent rework, additional expenses, trivial outcomes, and reduced confidence in sustainability practices. The current ESGA method selection process lacks comprehensive consideration of alternatives and criteria, occasionally resulting in suboptimal choices. This work aims to achieve optimal ESGA by engineering a method selection framework. The research approach is based on the design cycle, where engineering decisions are informed by empirical evidence. The main findings are that the framework, which includes a decision model and a supporting information system, can reduce the chances of organizations selecting an unsuitable method, whilst sparing decision-making managers time and effort. Firstly, the reusable elements of the framework can help managers of any organization select a suitable method more efficiently since they do not have to produce these elements themselves. Secondly, the results demonstrate how selection frameworks and tools can aid organizations in navigating the complex ESG accounting landscape. Lastly, this study lowers the barrier for organizational impact management; in particular, for measuring and reporting ESG impact, which is a rigorous assessment of the organization’s progress towards sustainable development goals

Requirements Template for Analytics Projects

Data analytics projects have become a common accomplishment in many enterprises. However, establishing a data analytics project requires consideration of many factors that are not always recognized at the very beginning of the project. This study seeks to identify what generic requirements must be defined for data analytics projects and what analytics project attributes need to be addressed by these requirements. It proposes a requirements template for the generic requirements of the analytics projects. The template is intended to be used to reduce the complexity of starting the analytics projects by providing a checklist of requirements to be considered at the beginning of the project. The template is derived from analyzing 16 data analytics project reports for descriptive, diagnostic, predictive, and prescriptive analytics tasks. The template is then validated by analyzing its compliance with 20 analytics projects within the real estate domain using the corresponding research articles

Editorial Introduction to Issue 39: Managing Complexity and Knowledge in Enterprise Projects

This issue of CSIMQ includes five articles selected on the topics of complexity management and knowledge management in enterprises from various perspectives and towards diverse goals: the economic and societal perspective seeking strategy fulfillment with the help of enterprise architecture management, the operational performance perspective, which requires grounding management decisions on data insights, and the human aspect perspective to facilitate collaboration, creativity and innovation. Methodologically, the issue reports a mix of conceptual modeling and analytics approaches, suggesting an emerging requirement to balance or alternate, in complexity management, abstraction-based analysis, and data-oriented analytics

Survey on Organizational Chat Conversation Analysis: Exploring Dialogue Summarization from a Knowledge Discovery Perspective

With the latest advances in natural language processing technologies, multi-participant conversation summarization features are now embedded in the most widely used collaboration platforms offered by industry leaders such as Microsoft, Google, and Zoom. This allows employees to streamline their work and increase efficiency by summarizing long chat threads. In this study, an attempt has been made to perceive summarized chat conversations as a tool for knowledge discovery and reusable information extraction within an organization in general or during projects. To this end, recent scientific articles have been reviewed to identify the most effective techniques and approaches for summarizing chat threads and conversations alongside the challenges and peculiarities of collaborative text-based communication. In addition, significant attention has been paid to the further utilization of the extracted information to represent the knowledge for further reuse

Enable Flexibilisation in FAIRWork’s Democratic AI-based Decision Support System by Applying Conceptual Models Using ADOxx

Decision-making in complex production environments is challenging as the information and knowledge requirements must be constantly observed since the ecosystems they operate in are continuously changing. Artificial intelligence (AI) can tackle complexity in decision-making by making machines more intelligent. But reacting to changing or new problems and related decision processes to facilitate the understanding of the involved humans is an equally important problem. Therefore, decision support systems are required to assist complex decisions and enable flexibility to support the decision-makers. Within this scope, we will introduce the Democratic AI-based Decision Support System (DAI-DSS), which is designed and implemented within the EU-funded FAIRWork project, considering both human and machine actors during decision-making. The FAIRWork project proposes a model-based approach to both express high-level decision scenarios and formally describe the decision processes, which are then used as input for configuring the decision support system to meet concrete decision problems

Assistance in Model Driven Development: Toward an Automated Transformation Design Process

Model driven engineering aims to shorten the development cycle by focusing on abstractions and partially automating code generation. We long lived in the myth of automatic Model Driven Development (MDD) with promising approaches, techniques, and tools. Describing models should be a main concern in software development as well as model verification and model transformation to get running applications from high level models. We revisit the subject of MDD through the prism of experimentation and open mindness. In this article, we explore assistance for the stepwise transition from the model to the code to reduce the time between the analysis model and implementation. The current state of practice requires methods and tools. We provide a general process and detailed transformation specifications where reverse-engineering may play its part. We advocate a model transformation approach in which transformations remain simple, the complexity lies in the process of transformation that is adaptable and configurable. We demonstrate the usefulness, and scalability of our proposed MDD process by conducting experiments. We conduct experiments within a simple case study in software automation systems. It is both representative and scalable. The models are written in UML; the transformations are implemented mainly using ATL, and the programs are deployed on Android and Lego EV3. Last we report the lessons learned from experimentation for future community work