Abstract
A fundamental problem in conformance checking is aligning event data with process models. Unfortunately, existing techniques for this task are either complex, or can only be applicable to restricted classes of models. This in practice means that for large inputs, current techniques often fail to produce a result. In this paper we propose a method to approximate alignments for unconstrained process models, which relies on the use of relaxation labelling techniques on top of a partial order representation of the process model. The implementation on the proposed technique achieves a speed-up of several orders of magnitude with respect to the approaches in the literature (either optimal or approximate), often with a reasonable trade-off on the cost of the obtained alignment.
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Notice that a transition can correspond to several different firing modes, that depend on the context, which will be represented as different events in the unfolding.
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In case models do not have duplicate labels, the detection of loops can alternatively be performed as it was done in [2].
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Notice that, for the sake of simplicity, the example in Fig. 2 only contains one unfolding event per label. In general several events in the unfolding can have the same label, and our technique handles that general case.
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This work has been supported by MINECO and FEDER funds under grant TIN2017-86727-C2-1-R.
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Padró, L., Carmona, J. (2019). Approximate Computation of Alignments of Business Processes Through Relaxation Labelling. In: Hildebrandt, T., van Dongen, B., Röglinger, M., Mendling, J. (eds) Business Process Management. BPM 2019. Lecture Notes in Computer Science(), vol 11675. Springer, Cham. https://doi.org/10.1007/978-3-030-26619-6_17
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