Abstract
The alignment of observed and modeled behavior is an essential aid for organizations, since it opens the door for root-cause analysis and enhancement of processes. The state-of-the-art technique for computing alignments has exponential time and space complexity, hindering its applicability for medium and large instances. Moreover, the fact that there may be multiple optimal alignments is perceived as a negative situation, while in reality it may provide a more comprehensive picture of the model’s explanation of observed behavior, from which other techniques may benefit. This paper presents a novel evolutionary technique for approximating multiple optimal alignments. Remarkably, the memory footprint of the proposed technique is bounded, representing an unprecedented guarantee with respect to the state-of-the-art methods for the same task. The technique is implemented into a tool, and experiments on several benchmarks are provided.
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Notes
- 1.
\(\mathcal{B}(\varSigma )\) denotes the set of all multisets of the set \(\varSigma \).
- 2.
As the reader will soon realize, we refer to the term fitness in the genetic algorithms context.
- 3.
In Petri net terms, missed tokens represent tokens that hamper the firing of a transition.
- 4.
Note that indeed the edit distance is computed between \(\sigma \) and \(\ell (\chi )\).
- 5.
The restriction on having the same Parikh vector is for the sake of simplicity of application.
- 6.
In case we have a limited number of observations, the real distribution can be estimated by traditional methods, like Kernel Smoothing [18].
- 7.
In case of duplicate labels in the traces, the normality assumption may be violated and therefore the estimation may be less accurate. In spite of this, the distribution used is only an oracle for generating new locations and does not limit the applicability or our approach.
- 8.
The gap penalty represents asynchronous move in our setting.
- 9.
The experiments have been done on Intel Core i7-2.20 GHz computer with 8 GB of RAM.
- 10.
- 11.
At the time of generating models for the experiments, PLG2 in fact was unable to produce models containing duplicate labels from scratch, therefore the generated models and logs were modified in order to have transitions with duplicate labels.
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Acknowledgments
We would like to thank B. van Dongen for interesting discussions. This work has been supported by MINECO and FEDER funds under grant TIN2017-86727-C2-1-R.
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Taymouri, F., Carmona, J. (2018). An Evolutionary Technique to Approximate Multiple Optimal Alignments. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds) Business Process Management. BPM 2018. Lecture Notes in Computer Science(), vol 11080. Springer, Cham. https://doi.org/10.1007/978-3-319-98648-7_13
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