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DisCoveR: accurate and efficient discovery of declarative process models

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Abstract

Declarative process modeling formalisms—which capture high-level process constraints—have seen growing interest, especially for modeling flexible processes. This paper presents DisCoveR, an efficient and accurate declarative miner for learning Dynamic Condition Response (DCR) Graphs from event logs. We present a precise formalization of the algorithm, describe a highly efficient bit vector implementation and present a preliminary evaluation against five other miners, representing the state-of-the-art in declarative and imperative mining. DisCoveR performs competitively with each of these w.r.t. a fully automated binary classification task, achieving an average accuracy of 96.1% in the Process Discovery Contest 2019 (Results are available at https://icpmconference.org/2019/process-discovery-contest). We appeal to computational learning theory to gain insight into its performance as a classifier. Due to its linear time complexity, DisCoveR also achieves much faster run times than other declarative miners. Finally, we show how the miner has been integrated in a state-of-the-art declarative process modeling framework as a model recommendation tool and discuss how discovery can play an integral part of the modeling task and report on how the integration has improved the modeling experience of end-users.

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Notes

  1. Effective, co-created and compliant adaptive case management for Knowledge workers.

  2. http://www.kppq.de/miner.html

  3. In order to completely capture AlternatePrecedence, the target activity needs to be excluded in the initial marking. This can lead to complications w.r.t. other relations in which the target is source, and is therefore omitted.

  4. https://icpmconference.org/2019/process-discovery-contest

  5. Independent and identically distributed.

  6. The minimal in-sample error achievable for hypothesis \(h \in {\mathcal {H}}\).

  7. http://pm4py.org

  8. http://www.kmd.dk/indsigter/fleksibilitet-og-dynamisk-sagsbehandling-i-staten

  9. https://ecoknow.org/

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Work supported by the Innovation Fund Denmark project EcoKnow (7050-00034A) and the Danish Council for Independent Research project Hybrid Business Process Management Technologies (DFF-6111-00337).

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Back, C.O., Slaats, T., Hildebrandt, T.T. et al. DisCoveR: accurate and efficient discovery of declarative process models. Int J Softw Tools Technol Transfer 24, 563–587 (2022). https://doi.org/10.1007/s10009-021-00616-0

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