Abstract
Modern business processes are often characterized by continuous change, which can lead to bias in the results of process mining techniques that assume a static process. This bias is caused by concept drift, which can manifest in many forms and affect various process perspectives. Current research on concept drift in process mining has focused on drift detection techniques in the control-flow perspective, with limited capabilities for comprehensive dynamic profiling of event logs. To address this gap, this paper presents the DyLoPro framework, a generic approach that facilitates the exploration of event log dynamics over time using visual analytics. The framework caters to all types of event logs and allows for the exploration of event log dynamics from various process perspectives, both individually and combined with the performance perspective. Additionally, the framework is accompanied by an efficient and user-friendly Python library, rendering it a valuable instrument for both researchers and practitioners. A case study using large real-life event logs demonstrates the effectiveness of the framework.
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Notes
- 1.
What a good value for p constitutes, depends on the arrival frequency of cases. Each resulting sublog should be populated enough such that the derived measures are representative, but not too populated, as aggregating over a too long period and/or over too many cases could level out interesting trends and patterns.
- 2.
For our proposed set of mapping functions displayed in Table 1, \(\xi \) is either 1 or 2. However, this framework is rather meant as a starting point, and its verbosity can be extended at the discretion of the user.
- 3.
The dimensionality K, which is constant for every vector \(\boldsymbol{M_i}\) \((i \in [1, \dots , \xi ])\), is completely determined by the concept-specific configuration parameters \(\boldsymbol{\theta }^{C^o}\). We do not include this in the notation for simplicity.
- 4.
For the DFRs concept, two measures are computed. One giving the relative fraction of cases in which \( dfr _k\) occurs at least once, and another one giving the aggregated amount of occurrences per case.
- 5.
Annotated notebooks with the most interesting visualizations for each event log can be found here: https://github.com/BrechtWts/DyLoPro_CaseStudies.
- 6.
- 7.
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Wuyts, B., Weytjens, H., vanden Broucke, S., De Weerdt, J. (2023). DyLoPro: Profiling the Dynamics of Event Logs. In: Di Francescomarino, C., Burattin, A., Janiesch, C., Sadiq, S. (eds) Business Process Management. BPM 2023. Lecture Notes in Computer Science, vol 14159. Springer, Cham. https://doi.org/10.1007/978-3-031-41620-0_9
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