@article {2022:sosym:bpmn,
	title = {Live process modeling with the BPMN Sketch Miner},
	journal = {Software and Systems Modeling},
	volume = {21},
	year = {2022},
	month = {October},
	pages = {1877{\textendash}1906},
	abstract = {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.},
	keywords = {BPMN, text to visual, Textual Modelling DSL},
	issn = {1619-1366},
	doi = {10.1007/s10270-022-01009-w},
	url = {https://link.springer.com/article/10.1007/s10270-022-01009-w$\#$citeas},
	author = {Ana Ivanchikj and Souhaila Serbout and Cesare Pautasso}
}
@conference {2019:bpm,
	title = {Sketching Process Models by Mining Participant Stories},
	booktitle = {BPM Forum},
	year = {2019},
	month = {September},
	pages = {3-19},
	publisher = {Springer},
	organization = {Springer},
	address = {Vienna, Austria},
	abstract = {Producing initial process models currently requires gathering knowledge from multiple process participants and using modeling tools to produce a visual representation. With traditional tools this can require significant effort and thus delay the feedback cycle where the initial model is validated and refined based on participants{\textquoteright} feedback. In this paper we aim at reducing the effort required to obtain an initial process model by applying existing process mining techniques to sample process logs obtained directly from process participants. To that end, we specify a simple domain-specific language to represent process log fragments with natural language, and we illustrate the architecture of a live modeling tool, which produces a draft representation of the control flow which is updated in real-time as the logs are written down. The draft models generated by the tool can later be refined and completed by the business analysts using traditional tools. We argue that our synthesis of mining with modeling could provide benefits both in terms of efficiency in generating the initial draft model, as well as in terms of reducing the cognitive load of the business analyst during the requirements gathering phase. 
},
	keywords = {Draft Process Model, Process Mining, Process Requirements, Textual Modelling DSL},
	doi = {10.1007/978-3-030-26643-1_1},
	author = {Ana Ivanchikj and Cesare Pautasso}
}
