Titlebook: Conceptual Modeling; 40th International C Aditya Ghose,Jennifer Horkoff,Joerg Evermann Conference proceedings 2021 Springer Nature Switzerl

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https://doi.org/10.1007/978-3-030-93612-9een possible without internet technology..Business model up-scaling often requires a different ICT architecture, because an up-scaled business model imposes different performance requirements. This new architecture needs investments and has different operational expenses than the old architecture an

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Conceptual Modeling978-3-030-89022-3Series ISSN 0302-9743 Series E-ISSN 1611-3349

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https://doi.org/10.1007/978-3-030-89022-3artificial intelligence; computer programming; computer science; computer systems; databases; engineering

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978-3-030-89021-6Springer Nature Switzerland AG 2021

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234989.jpg

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Conference proceedings 2021nt, in October 2021.. ..The 14 full and 18 short papers were carefully reviewed and selected from 85 submissions.  The conference presents topics on conceptual modeling, its foundations and applications. Celebrating its 40th anniversary this year, the overall theme of ER 2021 is: Conceptual Modeling in an Age of Uncertainty..

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Conceptualizing Bots in Robotic Process Automationustry. The industry-driven advancement also comes with disadvantages. Each vendor established their own terminology and ecosystem, impeding communication, integration, and comparisons between RPA systems. In consequence, terminology and concepts are heterogeneous and not well understood. As a result

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Freezing Sub-models During Incremental Process Discoverysides parameter tuning, there is no interaction between the user and the algorithm. Interactive process discovery allows the user to exploit domain knowledge and to guide the discovery process. Previously, an incremental discovery approach has been introduced where a model, considered to be under “c

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Modeling Adaptive Data Analysis Pipelines for Crowd-Enhanced Processeserised by high volumes and noisy information, therefore data analysis should be always preceded by a data preparation phase. Designing and testing data preparation pipelines requires considering requirements on cost, time, and quality of data extraction. In this work, we aim to propose a methodology

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