Titlebook: Conceptual Modeling; 37th International C Juan C. Trujillo,Karen C. Davis,Mong Li Lee Conference proceedings 2018 Springer Nature Switzerla

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0302-9743 in October 2018..The 30 full and 13 short papers presented together with 3 keynotes were carefully reviewed and selected from 151 submissions..This events covers a wide range of following topics: Conceptual modeling studies,  ontological modeling, semi-structured data modeling, process modeling and

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Towards Conceptual Models for Machine Learning Computationsata preparation, training and inference of the ML models. Our models aim to: . achieve better documentation of ML analytics . provide a foundation for a chain of trust in the ML analytics outcome . provide a lever to enforce ethical and legal constraints within the ML pipeline. Representational mode

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Empirical Comparison of Model Consistency Between Ontology-Driven Conceptual Modeling and Traditionay-driven conceptual modeling (ODCM) technique with the objective to understand how these techniques influence the consistency between the resulting conceptual models. To determine these differences, we first briefly discuss previous research efforts and compose our hypothesis. Next, this hypothesis

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A Conceptual Framework for Supporting Deep Exploration of Business Process Behaviorta included in the log. Recent studies suggest extracting data-rich event logs from databases or transaction logs. However, these event logs are at a very fine granularity level, substituting business-level activities by low-level database operations, and challenging data-aware process mining. To ad

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Towards Data Visualisation Based on Conceptual Modellingly, on tables of data - and ignores the conceptual model of the data. Domain experts, who are likely to be familiar with the conceptual model of their data, may find it hard to understand tabular data representations, and hence hard to select appropriate data transformations and visualisations to me