Proceedings Articles |
2021 |
Klinkmüller, Christopher; Seeliger, Alexander; Müller, Richard; Pufahl, Luise; Weber, Ingo: A Method for Debugging Process Discovery Pipelines to Analyze the Consistency of Model Properties. In: Polyvyanyy, Artem; Wynn, Moe Thandar; Looy, Amy Van; Reichert, Manfred (Ed.): Business Process Management, pp. 65–84, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-85469-0. (Type: Proceedings Article | Abstract | Links | BibTeX | Tags: discovery, process mining, sensitivity analysis, uncertainty analysis)@inproceedings{Klinkmueller2021, Event logs have become a valuable information source for business process management, e.g., when analysts discover process models to inspect the process behavior and to infer actionable insights. To this end, analysts configure discovery pipelines in which logs are filtered, enriched, abstracted, and process models are derived. While pipeline operations are necessary to manage log imperfections and complexity, they might, however, influence the nature of the discovered process model and its properties. Ultimately, not considering this possibility can negatively affect downstream decision making. We hence propose a framework for assessing the consistency of model properties with respect to the pipeline operations and their parameters, and, if inconsistencies are present, for revealing which parameters contribute to them. Following recent literature on software engineering for machine learning, we refer to it as debugging. From evaluating our framework in a real-world analysis scenario based on complex event logs and third-party pipeline configurations, we see strong evidence towards it being a valuable addition to the process mining toolbox. |
2019 |
Klinkmüller, Christopher; Müller, Richard; Weber, Ingo: Mining Process Mining Practices: An Exploratory Characterization of Information Needs in Process Analytics. In: 17th International Conference on Business Process Management, Vienna, Austria, 2019. (Type: Proceedings Article | Abstract | Links | BibTeX | Tags: process mining, qualitative content analysis, visual analytics)@inproceedings{Klinkmueller2019, Many business process management activities benefit from the investigation of event data. Thus, research, foremost in the field of process mining, has focused on developing appropriate analysis techniques, visual idioms, methodologies, and tools. Despite the enormous effort, the analysis process itself can still be fragmented and inconve- nient: analysts often apply various tools and ad-hoc scripts to satisfy information needs. Therefore, our goal is to better understand the spe- cific information needs of process analysts. To this end, we characterize and examine domain problems, data, analysis methods, and visualization techniques associated with visual representations in 71 analysis reports. We focus on the representations, as they are of central importance for understanding and conveying information derived from event data. Our contribution lies in the explication of the current state of practice, en- abling the evaluation of existing as well as the creation of new approaches and tools against the background of actual, practical needs. |
Publications
Proceedings Articles |
2021 |
A Method for Debugging Process Discovery Pipelines to Analyze the Consistency of Model Properties. In: Polyvyanyy, Artem; Wynn, Moe Thandar; Looy, Amy Van; Reichert, Manfred (Ed.): Business Process Management, pp. 65–84, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-85469-0. | :
2019 |
Mining Process Mining Practices: An Exploratory Characterization of Information Needs in Process Analytics. In: 17th International Conference on Business Process Management, Vienna, Austria, 2019. | :