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Live process modeling with the BPMN Sketch Miner

BPMN Sketch Miner is a modeling environment for generating visual business process models starting from constrained natural language textual input. Its purpose is to support business process modelers who need to rapidly sketch visual BPMN models during interviews and design workshops, where particip...

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Detalles Bibliográficos
Autores principales: Ivanchikj, Ana, Serbout, Souhaila, Pautasso, Cesare
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9525392/
https://www.ncbi.nlm.nih.gov/pubmed/36196214
http://dx.doi.org/10.1007/s10270-022-01009-w
Descripción
Sumario:BPMN Sketch Miner is a modeling environment for generating visual business process models starting from constrained natural language textual input. Its purpose is to support business process modelers who need to rapidly sketch visual BPMN models during interviews and design workshops, where participants should not only provide input but also give feedback on whether the sketched visual model represents accurately what has been described during the discussion. In this article, we present a detailed description of the BPMN Sketch Miner design decisions and list the different control flow patterns supported by the current version of its textual DSL. We also summarize the user study and survey results originally published in MODELS 2020 concerning the tool usability and learnability and present a new performance evaluation regarding the visual model generation pipeline under actual usage conditions. The goal is to determine whether it can support a rapid model editing cycle, with live synchronization between the textual description and the visual model. This study is based on a benchmark including a large number of models (1350 models) exported by users of the tool during the year 2020. The main results indicate that the performance is sufficient for a smooth live modeling user experience and that the end-to-end execution time of the text-to-model-to-visual pipeline grows linearly with the model size, up to the largest models (with 195 lines of textual description) found in the benchmark workload.