Abstract
Process mining has caught the attention of researchers and practioners. Because a wide variety of process mining techniques have been proposed, it is difficult to choose a suitable process mining algorithm for a given enterprise or application domain. Model rediscoverability of process mining algorithms has been proposed as a benchmark to address this issue. Given a process model (we call it original model) and its corresponding event log, the model rediscoverability is to measure how similar between the original model and the process model mined by the process mining algorithm. As evaluating available process mining algorithms against a large set of business process models is computationally expensive, some recent works have been done to accelerate the evaluation by only evaluating a portion of process models (the so-called reference models) and recommending the others via a regression model. The effect of the recommendation is highly dependent on the quality of the reference models. Nevertheless, choosing the significant reference models from a given model set is also time-consuming and ineffective. This paper generalizes a universal significant reference model set. Furthermore, this paper also proposes a selection of process model features to increase the accuracy of recommending process mining algorithm. Experiments using artificial and real-life datasets show that our proposed reference model set and selected features are practical and outperform the traditional ones.
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Guo, Q., Wen, L., Wang, J., Ding, Z., Lv, C. (2014). A Universal Significant Reference Model Set for Process Mining Evaluation Framework. In: Ouyang, C., Jung, JY. (eds) Asia Pacific Business Process Management. AP-BPM 2014. Lecture Notes in Business Information Processing, vol 181. Springer, Cham. https://doi.org/10.1007/978-3-319-08222-6_2
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DOI: https://doi.org/10.1007/978-3-319-08222-6_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08221-9
Online ISBN: 978-3-319-08222-6
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