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P349 - EMISA 2024 Enterprise Modelling and Information Systems Architectures

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  • Textdokument
    Describing Behavior Sequences of Fattening Pigs Using Process Mining
    (EMISA 2024, 2024) Lepsien, Arvid; Melfsen, Andreas; Bosselmann, Jan; Koschmider, Agnes; Hartung, Eberhard
    Process mining is a well-established technique for gaining insights into event data. It allows significant insights into event data in terms of identifying process anomalies, giving hints between as-is and to-be process states or making predictions based on data. Although process mining has been successfully applied in many application domains like healthcare, finance, and manufacturing, additional domains might also benefit from process mining like life and natural sciences. However, these domains mainly do not rely on structured business data that is expected as input for process mining algorithms. Rather, data from these domains first has to be efficiently pre-processed. This paper suggests process mining as an approach to identify behavioral patterns of fattening pigs from video data. The goal of this approach is to demonstrate that process mining might be a valuable tool for understanding the behavior of pigs by considering and analyzing their behavior sequences. Furthermore, additional insights can be gained in terms of temporal and spatial analysis about the division of the pig pen in functional areas. In this way, new implications might be found about pig behavior compared to existing state-of-the art approaches in the field.
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    Domain-Specific Conceptual Modeling
    (EMISA 2024, 2024) Paczona, Martin; Mayr, Heinrich C.; Prochart, Guenter
    This EMISA24 Current Research Talk is based on the authors' paper, recently published in the journal Data and Knowledge Engineering. We address the question of whether and how domain-specific modeling can increase productivity in the development of technical systems in an industrial setting. For, managers' decisions are ultimately based on whether or not the use of a new method pays off. The research was intertwined with a project in which we collaborated with a company to develop a domain-specific modeling method and tool for designing and implementing testbeds for electric vehicles.
  • Textdokument
    EMISA 2024 Complete Volume
    (EMISA 2024, 2024)
  • Textdokument
    Investigating the impact of representation features on decision model comprehension (Extended Abstract)
    (EMISA 2024, 2024) Djurica, Djordje; Kummer, Tyge F.; Mendling, Jan; Figl, Kathrin
    Decision models play a crucial role in the development of information systems for tasks such as system analysis and design, as well as compliance management. The effective presentation of these models is essential to ensure their accuracy and completeness. Existing research on their cognitive effectiveness remains inconclusive. Our study advances understanding by examining the detailed representation features of decision models, including type (tree versus table), structure (expanded versus frugal), and design (monochromatic versus colored). We demonstrate that the use of color can improve model-task fit, and that structural features can enhance comprehension. Utilizing eye-tracking, we analyzed the underlying mechanisms of these effects. Our findings provide valuable insights for cognitive information systems research and practical applications, offering guidance for both users and developers of decision models.
  • Textdokument
    Process Mining Meets Visual Analytics
    (EMISA 2024, 2024) Pufahl, Luise; Grohs, Michael; Klein, Lisa-Marie; Rehse, Jana-Rebecca
    This extended abstract summarizes a study on the visualization of conformance checking results in process mining, presented at HICSS 2023. Conformance checking compares intended and actual business process behaviors using IT system logs. Our study examined the visualization features of both academic and commercial process mining tools. We found these tools offer visualizations for quantifying conformance, breaking it down, localizing, and explaining deviations. However, there is a need for structured research on process analysts' visualization needs and the interaction between data, analysts, and visualizations.
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    Addressing the Log Representativeness Problem using Species Discovery (Extended Abstract)
    (EMISA 2024, 2024) Kabierski, Martin; Richter, Markus; Weidlich, Matthias
    Event logs are generated during the enactment of process-centric information systems and form the basis for optimization, monitoring, and enhancement initiatives of said systems. As such, they enable a data-driven and unbiased evaluation of the as-is state of the underlying processes. Yet, since at any time, event logs represent merely a sample of the whole possible behaviour of the information system, insights are only actionable should the event log be representative of the information system from which it is derived. Therefore, the question arises of how the representativeness of an event log$L with respect to its generative system P can be quantified, given that only L is present. In this work, we argue, that representativeness of an event log needs to be assessed with an intended analysis question in mind and discuss log completeness as one important facet of representativeness. We show how established estimators from biodiversity research can be utilized to quantify log completeness.
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    Event sequence analysis and visualization
    (EMISA 2024, 2024) Yeshchenko, Anton; Mendling, Jan
    Event sequence analysis is an important field of computer science due to its relevance to a diverse spectrum of application domains such as manufacturing, logistics, healthcare, financial services, education, to name but a few. Despite this broad relevance across these domains, it is striking to observe that techniques for event sequence data analysis have been developed rather independently in different fields of computer science. The most prominent research fields investigating the analysis of event sequence data are process mining and information visualization. Process mining has emerged as a subfield of research on workflow management systems. Its focus is the development of new techniques for automatic process discovery from event sequence data with the ambition to provide a meaningful and understandable summary of the behavior to the business process analyst. Information visualization is a field of computer graphics, which originated as a subfield of human–computer interaction. Its focus is on devising new techniques for visualizing event sequence data in a meaningful way such that analysts can effectively explore them. Typical representations frequently used in this field are timelines that plot conceptually related sequences of events over a time axis. As similar as the ambitions of these research areas may sound, it is surprising that there is hardly any exchange of ideas. Cross-references are scarce and mutual awareness and understanding is limited.1 All this makes research on event sequence analysis a fragmented field with scattered contributions. So far, the contributions from these two fields have neither been compared nor have they been mapped to an integrated framework. At this stage, it is not clear to which extent both fields have developed complementary concepts and insights. Such intransparency is problematic since it bears the risk of opportunities of integration are missed and concepts established in one field are independently reinvented in the other one. In this current research talk at EMISA 2024 based on a recent article, we develop such a framework that we call Event Sequence Visualization framework (ESeVis) and that gives due credit to the traditions of both fields. Our mapping study provides an integrated perspective on both fields and potential synergies for future research. In this way, our work contributes towards overcoming the fragmentation of research on event sequence data analysis.
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    PhD Proposal
    (EMISA 2024, 2024) Andreswari, Rachmadita
    Process mining techniques provide operational insights into work processes in various types of organizations. These processes handle sensitive data of customers, patients, students, or citizens and their results impact the lives and careers of these affected persons. So far, much of process mining research has focused on classical dimensions of performance such a cycle time or operational cost. What is missing is a primal consideration of ethical concerns such as fairness. Fairness is a recently researched concept in machine learning, which requires a deeper integration into process mining algorithms. This research addresses this requirement. To this end, it aims to analyze fairness concerns in process mining, to develop new process mining algorithms that integrate fairness concerns, and to evaluate them for their effectiveness. Methodologically, our research will build on guidelines for design science and algorithm engineering research. In this way, we will combine engineering research with empirical evaluations.
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    Towards a Threat Modeling Language for Vessel Navigation and Port Call Optimization - harborLang
    (EMISA 2024, 2024) Hacks, Simon
    This paper presents harborLang, a novel threat modeling language tailored for the maritime sector, built on the Meta Attack Language (MAL) framework. harborLang addresses the unique security challenges in maritime transport by enabling modeling and mitigation of potential threats. Through integrating specific maritime domain knowledge, harborLang empowers stakeholders to construct comprehensive threat models, enhancing decision-making and operational safety in seaports and vessel navigation.