TY - GEN
T1 - A knowledge-based integrated approach for discovering and repairing declare maps
AU - Maggi, F.M.
AU - Jagadeesh Chandra Bose, R.P.
AU - Aalst, van der, W.M.P.
PY - 2013
Y1 - 2013
N2 - Process mining techniques can be used to discover process models from event data. Often the resulting models are complex due to the variability of the underlying process. Therefore, we aim at discovering declarative process models that can deal with such variability. However, for real-life event logs involving dozens of activities and hundreds or thousands of cases, there are often many potential constraints resulting in cluttered diagrams. Therefore, we propose various techniques to prune these models and remove constraints that are not interesting or implied by other constraints. Moreover, we show that domain knowledge (e.g., a reference model or grouping of activities) can be used to guide the discovery approach. The approach has been implemented in the process mining tool ProM and evaluated using an event log from a large Dutch hospital. Even in such highly variable environments, our approach can discover understandable declarative models.
AB - Process mining techniques can be used to discover process models from event data. Often the resulting models are complex due to the variability of the underlying process. Therefore, we aim at discovering declarative process models that can deal with such variability. However, for real-life event logs involving dozens of activities and hundreds or thousands of cases, there are often many potential constraints resulting in cluttered diagrams. Therefore, we propose various techniques to prune these models and remove constraints that are not interesting or implied by other constraints. Moreover, we show that domain knowledge (e.g., a reference model or grouping of activities) can be used to guide the discovery approach. The approach has been implemented in the process mining tool ProM and evaluated using an event log from a large Dutch hospital. Even in such highly variable environments, our approach can discover understandable declarative models.
U2 - 10.1007/978-3-642-38709-8_28
DO - 10.1007/978-3-642-38709-8_28
M3 - Conference contribution
SN - 978-3-642-38708-1
T3 - Lecture Notes in Computer Science
SP - 433
EP - 448
BT - Advanced Information Systems Engineering (25th International Conference, CAiSE 2013, Valencia, Spain, June 17-21, 2013. Proceedings)
A2 - Salinesi, C.
A2 - Norrie, M.C.
A2 - Pastor, O.
PB - Springer
CY - Berlin
ER -